UNSUPERVISED LAND COVER CLASSIFICATION OF HYBRID POLSAR IMAGES USING DEEP NETWORK

Deep learning has proved to be highly efficient towards large scale, high dimensional data, rendering it to be an area of interest for researchers. Enormous volumes of satellite imagery enables us to utilise the benefits of a deep framework in the field of remote sensing. In this paper, we propose an unsupervised patch based learning method to cluster hybrid polarimetric SAR images We extract small patches from the image data set, and train VGG16 model with batch normalization using an entropy based loss function. Initially, the patches are segmented into three classes, namely, surface, volume, and double-bounce, which are defined with reference to the SAR scattering characteristics. We further classify volume into dense vegetation, and agricultural areas. Mixed classes, mainly covering the areas which have settlements surrounded by tall trees, are also observed. This technique gives an average accuracy of 89.70%.