Evaluation of the performance of SAR and SAR-optical fused dataset for crop discrimination

Mustak, SK and Uday, Gowri and Ramesh, Balaji and Praveen, Bushra (2019) Evaluation of the performance of SAR and SAR-optical fused dataset for crop discrimination. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 42 (3). pp. 563-571.

[img]
Preview
Text
IAPRSSIS_mustak_vol_43_3_2019.pdf - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview

Abstract

Crop discrimination and acreage play a vital role in interpreting the cropping pattern, statistics of the produce and market value of each product. Sultan Battery is an area where a large amount of irrigated and rainfed paddy crops are grown along with Rubber, Arecanut and Coconut. In addition, the northern region of Sultan Battery is covered with evergreen and deciduous forest. In this study, the main objective is to evaluate the performance of optical and Synthetic Aperture Radar (SAR)-optical hybrid fusion imageries for crop discrimination in Sultan Bathery Taluk of Wayanad district in Kerala. Seven land use classes such as paddy, rubber, coconut, deciduous forest, evergreen forest, water bodies and others land use (e.g., built-up, barren etc.) were selected based on literature review and local land use classification policy. Both Sentinel-2A (optical) and sentinel-1A (SAR) satellite imageries of 2017 for Kharif season were used for classification using three machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Classification and Regression Trees (CART). Further, the performance of these techniques was also compared in order to select the best classifier. In addition, spectral indices and textural matrices (NDVI, GLCM) were extracted from the image and best features were selected using the sequential feature selection approach. Thus, 10-fold cross-validation was employed for parameter tuning of such classifiers to select best hyperparameters to improve the classification accuracy. Finally, best features, best hyperparameters were used for final classification and accuracy assessment. The results show that SVM outperforms the RF and CART and similarly, Optical+SAR datasets outperforms the optical and SAR satellite imageries. This study is very supportive for the earth observation scientists to support promising guideline to the agricultural scientist, policy-makers and local government for sustainable agriculture practice.

Item Type: Article
Additional Information: Copyright of this article belongs to the authors
Uncontrolled Keywords: Machine Learning, Earth Observation, Kernel Optimization, Feature Selection, Land Use and Cover
Subjects: C Publications by ATREEians > G Journal Papers
Divisions: Publications by ATREEians > Journal Papers
Depositing User: The Library Officer
Date Deposited: 28 Aug 2019 07:12
Last Modified: 28 Aug 2019 07:12
URI: http://eprints.atree.org/id/eprint/647

Actions (login required)

View Item View Item