Polarimetric SAR Image Classification using Gaussian Context Transformer in Complex-Valued Convolutional Neural Networks

Abstract

There have been many advancements in the field of terrain classification usingPolarimetric SAR images/data. This Thesis explores different classical methodsas well as deep learning methods for this task. The Covariance matrix of landsample data is classified into different terrains such as various crops, urban areasor water, etc. The PolSAR covariance matrix has both amplitude and phasecomponents. Statistical techniques such as Wishart Classifier and Wishart MixtureModel with Conditional Random Field (WMM-CRF) approach exploit the inherentmathematical predispositions of the data while Deep Learning techniquessuch as Complex Valued-CNN and Squeeze and Excitation Networks utilize thebrilliance of neural networks to study correlation in spatial data as well as interchanneldependencies. There have been studies in order to retrofit deep learningmodels with components that can leverage the predetermined data patterns inany dataset. Gaussian Context Transformer is one such technique that allows theexploitation of inter-channel dependencies with predetermined mathematical inclinationswhile the rest of the model learns spatial-contextual parameters. Inorder to overcome noise, there are no available ground truth images, hence dataaugmentation is done with several image processing techniques such as Box-Carfilter, Lee-Sigma filter, and Mean-Shift filters can be used to downsize the effectsof the multiplicative noise as much as possible. The effects of Gaussian ContextTransformers and Data augmentation on one Indian land sample, namely, Mysoreand three European land samples, namely, Flevoland-7, Flevoland-15, and Landesshow promising results.