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Rochester Institute of Technology
1.
Yang, Jie.
Crime Scene Blood Evidence Detection Using Spectral Imaging.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2019, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/10118 
► Blood is the key evidence for forensic investigation because it carries critical information to help reconstruct the crime scene, confirm or exclude a suspect,…
(more)
▼ Blood is the key evidence for forensic investigation because it carries critical information to help reconstruct the crime scene, confirm or exclude a suspect, and analyze the timing of a crime. Conventional bloodstain detection uses chemical methods. Those methods require cautious sample preparations. They are destructive to samples in principle. Some of them are carcinogenic to investigators. They require experienced investigators and constrained conditions. Spectral imaging methods are an emerging technique for bloodstain detection in forensic science. It provides a non-destructive, non-contact, non-toxic, and real-time methodology for presumptive bloodstain searching, either in the field or in the laboratory.
This thesis prototyped two crime scene bloodstain detection imaging systems. The first generation crime scene imaging system is a LCTF based visible hyperspectral imaging system. Detection results of a simulated indoor crime scene show that bloodstains can be highlighted. However, this system has some drawbacks. First of all, it only records spectral information at 400 nm to 700 nm spectral range. Bloodstains on some substrates may not be detected. Second, it has a low SNR. This is mainly due to the low transmittance of the LCTF. Third, its FOV is only pm7 degrees from normal. Fourth, it cannot work continuously for a long time. The lighting module is attached next to the camera, which emits excessive heat and warms the camera quickly. Fifth, it is not calibrated into physical units such as radiance or reflectance. Therefore, a second generation crime scene imaging system was developed.
The second generation crime scene imaging system is a VNIR multispectral imaging system. It uses interference filters to construct the spectral bands. Blood reflectance spectral features were extracted from the comparison of blood and visually similar non-blood substances on various common found substrates. Three spectral features were used to construct the VNIR spectral bands of the multispectral imaging system. A linear regression pixel-wise model was used to enhance the spatial uniformity of the CMOS sensor. The lens falloff was corrected. The transmittance spectra of interference filters with various incident angles were calibrated. The system was calibrated to reflectance with the required 10% accuracy from first principle based modeling.
Two verification tests were carried using the MSI system. The first test is a systematic test where 9 substrates were laid radially symmetric to study the spectral shift effect introduced by the interference filters. Comparing with the reflectance error, the spectral shift is found to be not as influential for bloodstain detection using RBD, RX, and TAD methods. The result from the first principle based modeling agrees with the test. The second test is a semi-realistic indoor crime scene test where different sizes of blood spatters were directly applied to off-white carpet, dark carpet, door, and painted wall under various daytime environmental conditions. The…
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Bloodstain; Forensic; Hyperspectral; Imaging; Multispectral; Optical system
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APA (6th Edition):
Yang, J. (2019). Crime Scene Blood Evidence Detection Using Spectral Imaging. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10118
Chicago Manual of Style (16th Edition):
Yang, Jie. “Crime Scene Blood Evidence Detection Using Spectral Imaging.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/10118.
MLA Handbook (7th Edition):
Yang, Jie. “Crime Scene Blood Evidence Detection Using Spectral Imaging.” 2019. Web. 28 Feb 2021.
Vancouver:
Yang J. Crime Scene Blood Evidence Detection Using Spectral Imaging. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/10118.
Council of Science Editors:
Yang J. Crime Scene Blood Evidence Detection Using Spectral Imaging. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10118
Rochester Institute of Technology
2.
Peery, Tyler R.
System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2019, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/10192
► Cultural heritage imaging is becoming more common with the increased availability of more complex imaging systems, including multi- and hyperspectral imaging (MSI and HSI)…
(more)
▼ Cultural heritage imaging is becoming more common with the increased availability of more complex imaging systems, including multi- and hyperspectral imaging (MSI and HSI) systems. A particular concern with HSI systems is the broadband source required, regularly including infrared and ultraviolet spectra, which may cause fading or damage to a target. Guidelines for illumination of such objects, even while on display at a museum, vary widely from one another. Standards must be followed to assure the curator to allow imaging and ensure protection of the document. Building trust in the cultural heritage community is key to gaining access to objects of significant import, thus allowing scientists, historians, and the public to view digitally preserved representations of the object, and to allow further discovery of the object through spectral processing and analysis.
Imaging was conducted with a light level of 270 lux at variable ground sample distances (GSD’s). The light level was chosen to maintain a total dose similar to an hour’s display time at a museum, based on the United Kingdom standard for cultural heritage display, PAS 198:2012. The varying GSD was used as a variable to increase signal-to-noise ratios (SNR) or decrease total illumination time on a target. This adjustment was performed both digitally and physically, and typically results in a decrease in image quality, as the spatial resolution of the image decreases.
However, a technique called “panchromatic sharpening” was used to recover some of the spatial resolution. This method fuses a panchromatic image with good spatial resolution with a spectral image (either MSI or HSI) with poorer spatial resolution to construct a derivative spectral image with improved spatial resolution. Detector systems and additional methods of data capture to assist in processing of cultural heritage documents are investigated, with specific focus on preserving the physical condition of the potentially sensitive documents.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Cultural heritage; Hyperspectral imaging (HSI); Image quality; Low-light imaging; Panchromatic sharpening; Signal-to-noise ratio (SNR)
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APA (6th Edition):
Peery, T. R. (2019). System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10192
Chicago Manual of Style (16th Edition):
Peery, Tyler R. “System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/10192.
MLA Handbook (7th Edition):
Peery, Tyler R. “System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts.” 2019. Web. 28 Feb 2021.
Vancouver:
Peery TR. System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/10192.
Council of Science Editors:
Peery TR. System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10192
Rochester Institute of Technology
3.
Bai, Di.
A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2019, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/10264
► Hyperspectral image (HSI) classification has been used to identify material diversity in remote sensing images. Recently, hyperspectral imaging has been applied to historical artifact…
(more)
▼ Hyperspectral image (HSI) classification has been used to identify material diversity in remote sensing images. Recently, hyperspectral imaging has been applied to historical artifact studies. For example, the Gough Map, one of the earliest surviving maps of Britain, was imaged in 2015 using a hyperspectral imaging system while in the collection at the Bodleian Library, Oxford University. The collection of the HSI data was aimed at pigment analysis for the material diversity of its composition and potentially the timeline of its creation. Traditional methods used spectral unmixing and the spectral angle mapper to classify features in HSIs of historical artifact, those approaches are based only on spectral information of the HSIs. To make full use of both the spatial and spectral features, we developed a novel deep learning technique called 3D-SE-ResNet and applied it to five HSI datasets, including three HSI benchmarks, Indian Pines, Kennedy Space Center, University of Pavia and two HSIs of cultural heritage artifacts, the Gough Map and the Selden Map of China. We trained this deep learning framework to classify pigments in large HSIs with a limited amount of reference (labelled) data automatically. Meanwhile, different spatial and spectral input size and various hyper-parameters of the framework were evaluated. With much less effort and much higher efficiency, this is a breakthrough in object identification and classification in cultural heritage studies that leverages the spectral and spatial information contained in this imagery. Historical geographers, cartographic historians and other scholars will benefit from this work to analyze the pigment mapping of cultural heritage artifacts in the future.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: 3D-SE-ResNet; Deep learning; Historical artifacts; Hyperspectral imaging; Image classification; Pigment mapping
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APA (6th Edition):
Bai, D. (2019). A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10264
Chicago Manual of Style (16th Edition):
Bai, Di. “A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/10264.
MLA Handbook (7th Edition):
Bai, Di. “A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods.” 2019. Web. 28 Feb 2021.
Vancouver:
Bai D. A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/10264.
Council of Science Editors:
Bai D. A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10264
Rochester Institute of Technology
4.
Hagstrom, Shea T.
Voxel-Based LIDAR Analysis and Applications.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/8316
► One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In…
(more)
▼ One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In particular, the past few decades have been greatly beneficial to these systems because of increases in data collection speed and accuracy, as well as a reduction in the costs of components. These improvements allow modern airborne instruments to resolve sub-meter details, making them ideal for a wide variety of applications. Because LIDAR uses active illumination to capture 3D information, its output is fundamentally different from other modalities. Despite this difference, LIDAR datasets are often processed using methods appropriate for 2D images and that do not take advantage of its primary virtue of 3-dimensional data.
It is this problem we explore by using volumetric voxel modeling. Voxel-based analysis has been used in many applications, especially medical imaging, but rarely in traditional remote sensing. In part this is because the memory requirements are substantial when handling large areas, but with modern computing and storage this is no longer a significant impediment. Our reason for using voxels to model scenes from LIDAR data is that there are several advantages over standard triangle-based models, including better handling of overlapping surfaces and complex shapes. We show how incorporating system position information from early in the LIDAR point cloud generation process allows radiometrically-correct transmission and other novel voxel properties to be recovered. This voxelization technique is validated on simulated data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, a first-principles based ray-tracer developed at the
Rochester Institute of
Technology.
Voxel-based modeling of LIDAR can be useful on its own, but we believe its primary advantage is when applied to problems where simpler surface-based 3D models conflict with the requirement of realistic geometry. To show the voxel model's advantage, we apply it to several outstanding problems in remote sensing: LIDAR quality metrics, line-of-sight mapping, and multi-model fusion. Each of these applications is derived, validated, and examined in detail, and our results compared with other state-of-the-art methods. In most cases the voxel-based methods demonstrate superior results and are able to derive information not available to existing methods. Realizing these improvements requires only a shift away from traditional 3D model generation, and our results give a small indicator of what is possible. Many examples of possible areas for future improvement and expansion of algorithms beyond the scope of our work are also noted.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: 3D; Analysis; Applications; LADAR; LIDAR; Voxel
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APA (6th Edition):
Hagstrom, S. T. (2014). Voxel-Based LIDAR Analysis and Applications. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8316
Chicago Manual of Style (16th Edition):
Hagstrom, Shea T. “Voxel-Based LIDAR Analysis and Applications.” 2014. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/8316.
MLA Handbook (7th Edition):
Hagstrom, Shea T. “Voxel-Based LIDAR Analysis and Applications.” 2014. Web. 28 Feb 2021.
Vancouver:
Hagstrom ST. Voxel-Based LIDAR Analysis and Applications. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/8316.
Council of Science Editors:
Hagstrom ST. Voxel-Based LIDAR Analysis and Applications. [Doctoral Dissertation]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8316
Rochester Institute of Technology
5.
Kucer, Michal.
Representations and representation learning for image aesthetics prediction and image enhancement.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2020, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/10464
► With the continual improvement in cell phone cameras and improvements in the connectivity of mobile devices, we have seen an exponential increase in the…
(more)
▼ With the continual improvement in cell phone cameras and improvements in the connectivity of mobile devices, we have seen an exponential increase in the images that are captured, stored and shared on social media. For example, as of July 1st 2017 Instagram had over 715 million registered users which had posted just shy of 35 billion images. This represented approximately seven and nine-fold increase in the number of users and photos present on Instagram since 2012. Whether the images are stored on personal computers or reside on social networks (e.g. Instagram, Flickr), the sheer number of images calls for methods to determine various image properties, such as object presence or appeal, for the purpose of automatic image management and curation. One of the central problems in consumer photography centers around determining the aesthetic appeal of an image and motivates us to explore questions related to understanding aesthetic preferences, image enhancement and the possibility of using such models on devices with constrained resources.
In this dissertation, we present our work on exploring representations and representation learning approaches for aesthetic inference, composition ranking and its application to image enhancement. Firstly, we discuss early representations that mainly consisted of expert features, and their possibility to enhance Convolutional Neural Networks (CNN). Secondly, we discuss the ability of resource-constrained CNNs, and the different architecture choices (inputs size and layer depth) in solving various aesthetic inference tasks: binary classification, regression, and image cropping. We show that if trained for solving fine-grained aesthetics inference, such models can rival the cropping performance of other aesthetics-based croppers, however they fall short in comparison to models trained for composition ranking. Lastly, we discuss our work on exploring and identifying the design choices in training composition ranking functions, with the goal of using them for image composition enhancement.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Image aesthetics; Image cropping; Image enhancement
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APA (6th Edition):
Kucer, M. (2020). Representations and representation learning for image aesthetics prediction and image enhancement. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10464
Chicago Manual of Style (16th Edition):
Kucer, Michal. “Representations and representation learning for image aesthetics prediction and image enhancement.” 2020. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/10464.
MLA Handbook (7th Edition):
Kucer, Michal. “Representations and representation learning for image aesthetics prediction and image enhancement.” 2020. Web. 28 Feb 2021.
Vancouver:
Kucer M. Representations and representation learning for image aesthetics prediction and image enhancement. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2020. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/10464.
Council of Science Editors:
Kucer M. Representations and representation learning for image aesthetics prediction and image enhancement. [Doctoral Dissertation]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10464
6.
Fan, Lei.
Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2016, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9396
► Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each…
(more)
▼ Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc.
Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset.
One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique…
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Data fusion; Graph theory; Lidar; Machine learning; Spatial-spectral; Tensor
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APA (6th Edition):
Fan, L. (2016). Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9396
Chicago Manual of Style (16th Edition):
Fan, Lei. “Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing.” 2016. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/9396.
MLA Handbook (7th Edition):
Fan, Lei. “Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing.” 2016. Web. 28 Feb 2021.
Vancouver:
Fan L. Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2016. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/9396.
Council of Science Editors:
Fan L. Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing. [Doctoral Dissertation]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9396
7.
Sun, Weihua.
Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2013, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/956
► Scene development plays the first step for synthetic image generation using DIRSIG (The Digital Imaging and Remote Sensing Image Generation Model). Traditionally the scenes…
(more)
▼ Scene development plays the first step for synthetic image generation using DIRSIG (The Digital Imaging and Remote Sensing Image Generation Model). Traditionally the scenes are built manually; the procedure is very time consuming especially for complex urban scenes. The research focuses on contributing to the DIRSIG scene model development based on information retrieval from high-resolution multispectral images, such as WorldView-2 sensor imagery. The proposed approach takes advantage of a sequence of image processing routines to enhance the spectral images and extracts key geographical features for the man-made road network and naturally occurring water bodies. These routines take into account the spatial as well as spectral signatures in the multispectral images. They constitute a chained process, which includes several steps: pan-sharpening, image filtering, classification, segmentation, morphological processing, vectorization and final refinement. In the first step of the process, a novel and highly parallel nearest-neighbor diffusion based pan-sharpening procedure (NNDiffuse) is designed to fuse a high spatial resolution panchromatic image with the spectral image. Image filtering using trilateral filters for multispectral images is devised to process the image, removing small variances in the image as well as preserving significant edges. Spectral features such as Spectral Angle Mapper (SAM) are used to locate natural resource coverage such as water bodies. Multispectral flood fill technique, a graph based connected component technique and a knowledge-based system is used to extract the road networks. Both the road network and the water bodies can be refined and exported as vectorized ArcGIS shapefile. The outcome of the research is a workflow to facilitate scene development from spectral images; it also contributes to the development in the field of cartographic feature extraction, photogrammetry and target detection.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Feature extraction; Image fusion; Superresolution; Remote-sensing images – Data processing; Optical pattern recognition; Image analysis
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APA (6th Edition):
Sun, W. (2013). Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/956
Chicago Manual of Style (16th Edition):
Sun, Weihua. “Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images.” 2013. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/956.
MLA Handbook (7th Edition):
Sun, Weihua. “Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images.” 2013. Web. 28 Feb 2021.
Vancouver:
Sun W. Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2013. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/956.
Council of Science Editors:
Sun W. Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images. [Doctoral Dissertation]. Rochester Institute of Technology; 2013. Available from: https://scholarworks.rit.edu/theses/956
8.
Ziemann, Amanda K.
A manifold learning approach to target detection in high-resolution hyperspectral imagery.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2015, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/8617
► Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to…
(more)
▼ Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying "targets" such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m << d. In the remote sensing community, this has led to a recent increase in the use of manifold learning, which aims to characterize the embedded lower-dimensional, non-linear manifold upon which the hyperspectral data inherently lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation; the artificial target manifold helps to guide the separation of the target data from the background data in the new, lower-dimensional manifold coordinates. Then, target detection is performed in the manifold space.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Graph theory; Hyperspectral; Manifold learning; Target detection
…component
PCA Principal Components Analysis
RGB red, green, and blue
RIT Rochester Institute of… …Technology
ROI region of interest
RX Reed-Xiaoli Detector
14
LIST OF FIGURES
SAM Spectral Angle…
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APA (6th Edition):
Ziemann, A. K. (2015). A manifold learning approach to target detection in high-resolution hyperspectral imagery. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8617
Chicago Manual of Style (16th Edition):
Ziemann, Amanda K. “A manifold learning approach to target detection in high-resolution hyperspectral imagery.” 2015. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/8617.
MLA Handbook (7th Edition):
Ziemann, Amanda K. “A manifold learning approach to target detection in high-resolution hyperspectral imagery.” 2015. Web. 28 Feb 2021.
Vancouver:
Ziemann AK. A manifold learning approach to target detection in high-resolution hyperspectral imagery. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2015. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/8617.
Council of Science Editors:
Ziemann AK. A manifold learning approach to target detection in high-resolution hyperspectral imagery. [Doctoral Dissertation]. Rochester Institute of Technology; 2015. Available from: https://scholarworks.rit.edu/theses/8617
9.
Lewis, Christian M.
The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations.
Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/8324
► Activity Based Intelligence (ABI) is the derivation of information from a series of in- dividual actions, interactions, and transactions being recorded over a period…
(more)
▼ Activity Based Intelligence (ABI) is the derivation of information from a series of in- dividual actions, interactions, and transactions being recorded over a period of time. This usually occurs in Motion imagery and/or Full Motion Video. Due to the growth of unmanned aerial systems
technology and the preponderance of mobile video devices, more interest has developed in analyzing people's actions and interactions in these video streams. Currently only visually subjective quality metrics exist for determining the utility of these data in detecting specific activities. One common misconception is that ABI boils down to a simple resolution problem; more pixels and higher frame rates are better. Increasing resolution simply provides more data, not necessary more informa- tion. As part of this research, an experiment was designed and performed to address this assumption. Nine sensors consisting of four modalities were place on top of the Chester F. Carlson Center for Imaging Science in order to record a group of participants executing a scripted set of activities. The multimodal characteristics include data from the visible, long-wave infrared, multispectral, and polarimetric regimes. The activities the participants were scripted to cover a wide range of spatial and temporal interactions (i.e. walking, jogging, and a group sporting event). As with any large data acquisition, only a subset of this data was analyzed for this research. Specifically, a walking object exchange scenario and simulated RPG. In order to analyze this data, several steps of preparation occurred. The data were spatially and temporally registered; the individual modalities were fused; a tracking algorithm was implemented, and an activity detection algorithm was applied. To develop a performance assessment for these activities a series of spatial and temporal degradations were performed. Upon completion of this work, the ground truth ABI dataset will be released to the community for further analysis.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Activity based intelligence; Activity recognition; Computer vision; Full motion video; Motion imagery; Multimodal (multispectral; polarimetric)
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APA (6th Edition):
Lewis, C. M. (2014). The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8324
Chicago Manual of Style (16th Edition):
Lewis, Christian M. “The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations.” 2014. Masters Thesis, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/8324.
MLA Handbook (7th Edition):
Lewis, Christian M. “The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations.” 2014. Web. 28 Feb 2021.
Vancouver:
Lewis CM. The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations. [Internet] [Masters thesis]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/8324.
Council of Science Editors:
Lewis CM. The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations. [Masters Thesis]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8324
10.
Sun, Jiangqin.
Temporal Signature Modeling and Analysis.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/8506
► A vast amount of digital satellite and aerial images are collected over time, which calls for techniques to extract useful high-level information, such as…
(more)
▼ A vast amount of digital satellite and aerial images are collected over time, which calls for techniques to extract useful high-level information, such as recognizable events. One part of this thesis proposes a framework for streaming analysis of the time series, which can recognize events without supervision and memorize them by building the temporal contexts. The memorized historical data is then used to predict the future and detect anomalies. A new incremental clustering method is proposed to recognize the event without training. A memorization method of double localization, including relative and absolute localization, is proposed to model the temporal context. Finally, the predictive model is built based on the method of memorization. The "Edinburgh Pedestrian Dataset", which offers about 1000 observed trajectories of pedestrians detected in camera images each working day for several months, is used as an example to illustrate the framework.
Although there is a large amount of image data captured, most of them are not available to the public. The other part of this thesis developed a method of generating spatial-spectral-temporal synthetic images by enhancing the capacity of a current tool called DIRISG (Digital Imaging and Remote Sensing Image Generation). Currently, DIRSIG can only model limited temporal signatures. In order to observe general temporal changes in a process within the scene, a process model, which links the observable signatures of interest temporally, should be developed and incorporated into DIRSIG. The sub process models could be categorized into two types. One is that the process model drives the property of each facet of the object changing over time, and the other one is to drive the geometry location of the object in the scene changing as a function of time. Two example process models are used to show how process models can be incorporated into DIRSIG.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Clustering; Data mining; Image simulation; Parking lot model; Predictive models; Temporal modeling
…During the 2012 summer, RIT (Rochester Institute of Technology)
performed a large… …Imaging and Remote Sensing Laboratory) at Rochester Institute of Technology has spent the…
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APA (6th Edition):
Sun, J. (2014). Temporal Signature Modeling and Analysis. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8506
Chicago Manual of Style (16th Edition):
Sun, Jiangqin. “Temporal Signature Modeling and Analysis.” 2014. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/8506.
MLA Handbook (7th Edition):
Sun, Jiangqin. “Temporal Signature Modeling and Analysis.” 2014. Web. 28 Feb 2021.
Vancouver:
Sun J. Temporal Signature Modeling and Analysis. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/8506.
Council of Science Editors:
Sun J. Temporal Signature Modeling and Analysis. [Doctoral Dissertation]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8506
11.
Stoddard, Jordyn.
Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality.
Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/8308
► The adoption of cube-satellites (cubesats) by the space community has drastically lowered the cost of access to space and reduced the development lifecycle from…
(more)
▼ The adoption of cube-satellites (cubesats) by the space community has drastically lowered the cost of access to space and reduced the development lifecycle from the hundreds of millions of dollars spent on traditional decade-long programs. Rapid deployment and low cost are attractive features of cubesat-based imaging that are conducive to applications such as disaster response and monitoring. One proposed application is 3D surface modeling through a high revisit rate constellation of cubesat imagers. This work begins with the characterization of an existing design for a cubesat imager based on ground sampled distance (GSD), signal-to-noise ratio (SNR), and smear. From this characterization, an existing 3D workflow is applied to datasets that have been degraded within the regime of spatial resolutions and signal-to-noise ratios anticipated for the cubesat imager. The fidelity of resulting point clouds are assessed locally for both an urban and a natural scene. The height of a building and normals to its surfaces are calculated from the urban scene, while quarry depth estimates and rough volume estimates of a pile of rocks are produced from the natural scene. Though the reconstructed scene geometry and completeness of the scene suffer noticeably from the degraded imagery, results indicate that useful information can still be extracted using some of these techniques up to a simulated GSD of 2 meters.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: 3D reconstuction; Cubesats; Image quality; Point clouds; Structure from motion
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APA (6th Edition):
Stoddard, J. (2014). Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8308
Chicago Manual of Style (16th Edition):
Stoddard, Jordyn. “Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality.” 2014. Masters Thesis, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/8308.
MLA Handbook (7th Edition):
Stoddard, Jordyn. “Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality.” 2014. Web. 28 Feb 2021.
Vancouver:
Stoddard J. Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality. [Internet] [Masters thesis]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/8308.
Council of Science Editors:
Stoddard J. Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality. [Masters Thesis]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8308
12.
Albano, James A.
Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2013, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/5951
► Identification of materials from calibrated radiance data collected by an airborne imaging spectrometer depends strongly on the atmospheric and illumination conditions at the time…
(more)
▼ Identification of materials from calibrated radiance data collected by an airborne imaging spectrometer depends strongly on the atmospheric and illumination conditions at the time of collection. This thesis demonstrates a methodology for identifying material spectra using the assumption that each unique material class forms a lower-dimensional manifold (surface) in the higher-dimensional spectral radiance space and that all image spectra reside on, or near, these theoretic manifolds. Using a physical model, a manifold characteristic of the target material exposed to varying illumination and atmospheric conditions is formed. A graph-based model is then applied to the radiance data to capture the intricate structure of each material manifold, followed by the application of the commute time distance (CTD) transformation to separate the target manifold from the background. Detection algorithms are then applied in the CTD subspace. This nonlinear transformation is based on a random walk on a graph and is derived from an eigendecomposition of the pseudoinverse of the graph Laplacian matrix. This work provides a geometric interpretation of the CTD transformation, its algebraic properties, the atmospheric and illumination parameters varied in the physics-based model, and the influence the target manifold samples have on the orientation of the coordinate axes in the transformed space.
This thesis concludes by demonstrating improved detection results in the CTD subspace as compared to detection in the original spectral radiance space.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Remote sensing – Data processing; Spectrometer – Data processor; Multispectral photography
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APA (6th Edition):
Albano, J. A. (2013). Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/5951
Chicago Manual of Style (16th Edition):
Albano, James A. “Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique.” 2013. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/5951.
MLA Handbook (7th Edition):
Albano, James A. “Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique.” 2013. Web. 28 Feb 2021.
Vancouver:
Albano JA. Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2013. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/5951.
Council of Science Editors:
Albano JA. Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique. [Doctoral Dissertation]. Rochester Institute of Technology; 2013. Available from: https://scholarworks.rit.edu/theses/5951
13.
Harris, Michael L.
Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features.
Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/8542
► RIT's Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool allows modeling of real world scenes to create synthetic imagery for sensor design and…
(more)
▼ RIT's Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool allows modeling of real world scenes to create synthetic imagery for sensor design and analysis, trade studies, algorithm validation, and training image analysts. To increase model construction speed, and the diversity and size of synthetic scenes which can be generated it is desirable to automatically segment real world imagery into different material types and import a material classmap into DIRSIG.
This work contributes a methodology based on standard texture recognition techniques to supervised classification of material types in oblique aerial imagery. Oblique imagery provides many challenges for texture recognition due to illumination changes with view angle, projective distortions, occlusions and self shadowing. It is shown that features derived from a set of rotationally invariant bandpass filters fused with color channel information can provide supervised classification accuracies up to 70% with minimal training data.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Filter; Gabor; Machine learning; Pattern recognition; Supervised classification
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APA (6th Edition):
Harris, M. L. (2014). Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8542
Chicago Manual of Style (16th Edition):
Harris, Michael L. “Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features.” 2014. Masters Thesis, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/8542.
MLA Handbook (7th Edition):
Harris, Michael L. “Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features.” 2014. Web. 28 Feb 2021.
Vancouver:
Harris ML. Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features. [Internet] [Masters thesis]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/8542.
Council of Science Editors:
Harris ML. Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features. [Masters Thesis]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8542
14.
Dorado-Munoz, Leidy P.
Spectral Target Detecting Using Schroedinger Eigenmaps.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2016, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9186
► Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific…
(more)
▼ Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific objects or materials, usually few or small, which are surrounded by other materials that clutter the scene and hide the relevant information. This target detection process has been boosted lately by the use of hyperspectral imagery (HSI) since its high spectral dimension provides more detailed spectral information that is desirable in data exploitation. Typical spectral target detectors rely on statistical or geometric models to characterize the spectral variability of the data. However, in many cases these parametric models do not fit well HSI data that impacts the detection performance.
On the other hand, non-linear transformation methods, mainly based on manifold learning algorithms, have shown a potential use in HSI transformation, dimensionality reduction and classification. In target detection, non-linear transformation algorithms are used as preprocessing techniques that transform the data to a more suitable lower dimensional space, where the statistical or geometric detectors are applied. One of these non-linear manifold methods is the Schroedinger Eigenmaps (SE) algorithm that has been introduced as a technique for semi-supervised classification. The core tool of the SE algorithm is the Schroedinger operator that includes a potential term that encodes prior information about the materials present in a scene, and enables the embedding to be steered in some convenient directions in order to cluster similar pixels together.
A completely novel target detection methodology based on SE algorithm is proposed for the first time in this thesis. The proposed methodology does not just include the transformation of the data to a lower dimensional space but also includes the definition of a detector that capitalizes on the theory behind SE. The fact that target pixels and those similar pixels are clustered in a predictable region of the low-dimensional representation is used to define a decision rule that allows one to identify target pixels over the rest of pixels in a given image. In addition, a knowledge propagation scheme is used to combine spectral and spatial information as a means to propagate the \potential constraints" to nearby points. The propagation scheme is introduced to reinforce weak connections and improve the separability between most of the target pixels and the background. Experiments using different HSI data sets are carried out in order to test the proposed methodology. The assessment is performed from a quantitative and qualitative point of view, and by comparing the SE-based methodology against two other detection methodologies that use linear/non-linear algorithms as transformations and the well-known Adaptive Coherence/Cosine Estimator (ACE) detector. Overall results show that the SE-based detector outperforms the other two detection methodologies, which indicates the usefulness of…
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Image/data processing; Machine learning; Multispectral imaging; Target/object detection
…the electromagnetic spectrum
RIT
Rochester Institute of Technology
ROC
Receiver Operating…
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APA (6th Edition):
Dorado-Munoz, L. P. (2016). Spectral Target Detecting Using Schroedinger Eigenmaps. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9186
Chicago Manual of Style (16th Edition):
Dorado-Munoz, Leidy P. “Spectral Target Detecting Using Schroedinger Eigenmaps.” 2016. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/9186.
MLA Handbook (7th Edition):
Dorado-Munoz, Leidy P. “Spectral Target Detecting Using Schroedinger Eigenmaps.” 2016. Web. 28 Feb 2021.
Vancouver:
Dorado-Munoz LP. Spectral Target Detecting Using Schroedinger Eigenmaps. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2016. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/9186.
Council of Science Editors:
Dorado-Munoz LP. Spectral Target Detecting Using Schroedinger Eigenmaps. [Doctoral Dissertation]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9186
Rochester Institute of Technology
15.
Kwong, Justin.
Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation.
Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2009, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9083
► This project is an application of remote sensing techniques to the field of archaeology. Clustering and unmixing algorithms are applied to hyperspectral Hyperion imagery…
(more)
▼ This project is an application of remote sensing techniques to the field of archaeology. Clustering and unmixing algorithms are applied to hyperspectral Hyperion imagery over Oaxaca, Mexico. Oaxaca is the birthplace of the Zapotec civilization, the earliest state-level society in Mesoamerica. A passionate debate is ongoing over whether the Zapotecs' evolution was environmentally deterministic or socioeconomic. Previous archaeological remote sensing has focused on the difficult tasks of feature detection using low spatial resolution imagery or visual inspection of spectral data. This project attempts to learn about a civilization on the macro level, using unsupervised land classification techniques. Overlapping 158 band Hyperion data are tasked for approximately 30,000 km
2, to be taken over several years. K-means and ISODATA are implemented for clustering. MaxD is used to find endmembers for stepwise spectral unmixing. Case studies are performed that provide insights into the best use of various algorithms. To produce results with spatial context, a method is devised to tile long hyperspectral flight lines, process them, then merge the tiles back into a single coherent image. Google Earth is utilized to effectively share the produced classification and abundance maps. All the processes are automated to efficiently handle the large amount of data. In summary, this project focuses on spectral over spatial exploitation for a land survey study, using open source tools to facilitate results. Classification and abundance maps are generated highlighting basic material spatial patterns (
e.g., soil, vegetation and water). Additional remote sensing techniques that are potentially useful to archaeologists are briefly described for use in future work.
Advisors/Committee Members: David Messinger.
Subjects/Keywords: Gradient flow; Hyperion; Hyperspectral; MaxD; Stepwise unmixing; Zapotec
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APA (6th Edition):
Kwong, J. (2009). Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9083
Chicago Manual of Style (16th Edition):
Kwong, Justin. “Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation.” 2009. Masters Thesis, Rochester Institute of Technology. Accessed February 28, 2021.
https://scholarworks.rit.edu/theses/9083.
MLA Handbook (7th Edition):
Kwong, Justin. “Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation.” 2009. Web. 28 Feb 2021.
Vancouver:
Kwong J. Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation. [Internet] [Masters thesis]. Rochester Institute of Technology; 2009. [cited 2021 Feb 28].
Available from: https://scholarworks.rit.edu/theses/9083.
Council of Science Editors:
Kwong J. Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation. [Masters Thesis]. Rochester Institute of Technology; 2009. Available from: https://scholarworks.rit.edu/theses/9083
. 
Open Access Theses and Dissertations
