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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21342
Title: Crop classification on single date sentinel-2 imagery using random forest and suppor vector machine
Authors: Saini R.
Ghosh, Sanjay Kumar
Saran S.
Padalia H.
Kumar A.S.
Published in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
2018 ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People
Abstract: Mapping of the crop using satellite images is a challenging task due to complexities within field, and having the similar spectral properties with other crops in the region. Recently launched Sentinel-2 satellite has thirteen spectral bands, fast revisit time and resolution at three different level (10m, 20m, 60m), as well as the free availability of data, makes it a good choice for vegetation mapping. This study aims to classify crop using single date Sentinel-2 imagery in the Roorkee, district Haridwar, Uttarakhand, India. Classification is performed by using two most popular and efficient machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM). In this study, four spectral bands, i.e., Near Infrared, Red, Green, and Blue of Sentinel-2 satellite are stacked for the classification. Results show that overall accuracy of the classification achieved by RF and SVM using Sentinel-2 imagery are 84.22% and 81.85% respectively. This study demonstrates that both classifiers performed well by setting an optimal value of tuning parameters, but RF achieved 2.37% higher overall accuracy over SVM. Analysis of the results states that the class specific accuracies of High-Density Forest attain the highest accuracy whereas Fodder class reports the lowest accuracy. Fodder achieve lowest accuracy because there is an intermixing of pixels among Wheat and Fodder crops. In this study, it is found that RF shows better potential in classifying crops more accurately in comparison to SVM and Sentinel-2 has great potential in vegetation mapping domain in remote sensing. ¬© 2018 International Society for Photogrammetry and Remote Sensing. All Rights Reserved.
Citation: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (2018), 42(5): 683-688
URI: http://repository.iitr.ac.in/handle/123456789/21342
Issue Date: 2018
Publisher: International Society for Photogrammetry and Remote Sensing
Keywords: Crop classification
Machine learning
Random Forest
Sentinel-2
Support Vector Machine
Vegetation mapping
ISSN: 16821750
Author Scopus IDs: 57212506186
55478984700
Author Affiliations: Saini, R., Department of Computer Science, G. B. Pant Engineering College, Pauri, 246001, India, Geomatics Engineering Group, Department of Civil Engineering, IIT Roorkee247667, India
Ghosh, S.K., Geomatics Engineering Group, Department of Civil Engineering, IIT Roorkee247667, India
Corresponding Author: Saini, R.; Department of Computer Science, India; email: 2rashmisaini@gmail.com
Appears in Collections:Conference Publications [CE]

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