Development of generalized machine learning model to classify polsar data

In recent times, Polarimetric Synthetic Aperture Radar (PolSAR) data is available free of cost due to missions like UAVSAR and Sentinel. As ample data is available the applications are infinite. Till today many researchers have developed various techniques to classify PolSAR data efficiently. They have proposed classification techniques for which the ground truth should be available to train the classifier and validate the results for a particular geographic area. Training the classifier for each area is a time-consuming task and hence there is a need to develop a generalized model which can classify any geographical area acquired from a specific sensor for various land-cover features like water, settlement, forest, wetlands etc. In this paper a generalized machine learning model is proposed which can classify the data acquired from ALOS-PALSAR-2 L-band, irrespective of geographical area for the same land cover features. ANN classifier is used in this work. The classifier is trained using Mumbai and tested for San Francisco and Delhi data. It is observed that the classification accuracy for San Francisco as well as New Delhi is high.