Development of a generalized model to classify various land covers for ALOS-2 L-Band images using semantic segmentation.

For a long time, Polarimetric Synthetic Aperture Radar (PolSAR) data was not available free of cost, so the applications were limited. With the recent increase in availability of PolSAR data due to missions like ALOS-2, UAVSAR and Sentinel, the data can be acquired through these sensors periodically. Since the volume of data is large, applying traditional classifiers and conventional machine learning algorithms becomes confounding, as in that case, manual feature extraction must be done by the researcher for training the model. To automate this feature extraction step and accelerate the process, many researchers have used various deep learning algorithms. However, most studies fail to tap into the potential of PolSAR data by not utilizing the complete range of complex data of float-32 bit-depth. The work in this paper suggests the development of a generalized deep learning model for nine elements of coherency matrix [ T 3 ] in the float-32 data-space from ALOS/PALSAR-2 L-Band sensor. Semantic segmentation is used to classify land-covers into various classes like water, settlement, forest, open land, wetlands etc. This is done through modification on the UNet backbone. The deep learning model is trained using data on San Francisco and New Delhi regions and tested on data from Mumbai region. It is observed that the classification accuracy for Mumbai region is " 93.82 % ". This kind of system can assist the decision makers like urban planners to take informed decisions. [ABSTRACT FROM AUTHOR]