InSAR coherence-based land cover classification of Okara, Pakistan

Reliable and current availability of land cover knowledge are essential for many studies regarding planning, management, monitoring and updating activities. The optical satellite sensor data has been utilized for the classification of land use/land cover. In this study, the capability of synthetic aperture radar (SAR) interferometric coherence is practiced for land cover classification in Okara, Pakistan using Sentinel-1A imagery. Two Single Look Complex (SLC) product of months April and May 2016 were used and processed to create backscatter and interferometric coherence layers. From backscatter layers of each month, the mean backscatter and backscatter difference layer were obtained. False color composite (FCC) were developed comprising mean backscatter, backscatter difference and coherence, and performed supervised classification using maximum likelihood method to generate land cover classes i.e. water, barren, vegetation and built-up. Kappa statistics were employed for accuracy assessment of the output map. Results showed the good potential of Sentinel-1C-band for land cover classification having 0.69 Kappa coefficient and 80% overall accuracy. This study investigated the potential of C-band backscatter coefficients and coherence map for land cover discrimination. Coherence proved to be efficient in the examination of vegetative and non-vegetative areas. (C) 2017 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V.