Surface Area Classification Using Sentinel-1 SAR Backscattering Coefficients

Surface Area Classification Using Sentinel1 SAR Backscattering Coefficients
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© 2021 by IJETT Journal
Volume-69 Issue-12
Year of Publication : 2021
Authors : Prachi Kaushik, Suraiya Jabin
DOI :  10.14445/22315381/IJETT-V69I12P206

How to Cite?

Prachi Kaushik, Suraiya Jabin, "Surface Area Classification Using Sentinel1 SAR Backscattering Coefficients," International Journal of Engineering Trends and Technology, vol. 69, no. 12, pp. 39-46, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I12P206

Abstract
The Sentinel-1 synthetic aperture radar satellite captures high-resolution images of land and sea. The land cover classification of the earth`s surface can be done by analyzing the backscatter values recorded by the sensor. The images are calibrated to convert the digital number of each pixel into backscattering coefficients. For this study, the co-polarized and cross-polarized bands for the land cover classes were collected for the test area of Bhiwani. This enables the mathematical analysis of the bands to study the dependency of the coefficients for different land cover categories. There is a significant difference between the backscatter coefficients for the categories viz. apartments, villages, low-rise buildings, forests, parks, agricultural land, and water. Statistical analysis is done to study the level of correlation between class categories. Apartments and urban areas show a strong positive correlation of 0.992, indicating that apartments are strongly related to urban areas. A weak positive correlation of 0.288 between apartments and villages indicates that apartment-like structures are less likely in rural areas. Further, a few classification models for surface area classification were trained in seven categories, out of which ensemble bagged trees model performed with an accuracy of 95.3 percent on the test data.

Keywords
backscatter, SAR, Cubic SVM, ensemble, calibration, MATLAB

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