San Jose State University
MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE.
Degree: MS, Computer Science, 2019, San Jose State University
Recently, there has been a remarkable growth in Artificial Intelligence (AI) with
the development of efficient AI models and high-power computational resources for processing complex datasets. There has been a growing number of applications of machine learning in satellite remote sensing image data processing. In this work, machine learning methods were applied for crop classification of temporal multi- spectral satellite image to achieve better prediction of crop-wise area statistics. In India, agriculture has a huge impact on the national economy and most of the critical decisions are dependent on agricultural statistics. Sentinel-2 satellite image data for the Guntur district region of Andhra Pradesh, India has been used as the study area. The main reason for selecting this region is the diversity of agricultural crops and the availability of ground truth. As a baseline, the performance of machine learning models like Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbor (KNN) on crop classification was evaluated on 2016 single time multi-spectral satellite image. SVM performed well with an F1 score of 0.94. Further, the performance of SVM, RF, 1D and 2D Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and RNN with Gated Recurrent Unit (GRU) on 2018 Kharif season temporal multi-spectral satellite image has been evaluated for estimation of crop areas. The results show that SVM has the best F1 score of 0.99 and achieved a 95.9% agreement with the ground surveyed crop areas.
Advisors/Committee Members: Teng Moh, Chris Pollett, Thomas Austin.
Subjects/Keywords: crop classification; india; machine learning models; satellite images; Artificial Intelligence and Robotics; Other Computer Sciences
to Zotero / EndNote / Reference
APA (6th Edition):
Koppaka, R. (2019). MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE. (Masters Thesis). San Jose State University. Retrieved from https://scholarworks.sjsu.edu/etd_projects/734
Chicago Manual of Style (16th Edition):
Koppaka, Ravali. “MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE.” 2019. Masters Thesis, San Jose State University. Accessed June 24, 2019.
MLA Handbook (7th Edition):
Koppaka, Ravali. “MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE.” 2019. Web. 24 Jun 2019.
Koppaka R. MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2019 Jun 24].
Available from: https://scholarworks.sjsu.edu/etd_projects/734.
Council of Science Editors:
Koppaka R. MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE. [Masters Thesis]. San Jose State University; 2019. Available from: https://scholarworks.sjsu.edu/etd_projects/734