Edge-Preserving classification of polarimetric SAR images using Wishart distribution and conditional random field

Abstract

Classification of polarimetric SAR images into different ground covers has important applications in fields such as land mapping, agriculture monitoring, and assessment. The Wishart supervised classifier is one of the most widely used and general purpose classifier for polarimetric SAR data. However, it is a pixel-based classifier, so the performance is greatly affected by inherent speckle noise. The impact of speckle noise can be reduced by considering the spatial information from neighbouring pixels for classification tasks. In this paper, we aim to improve classification results by incorporating spatial-contextual information along with preservation of significant details such as edges and micro-regions. For this purpose, a conditional random field (CRF) based model is proposed for polarimetric SAR data along with Wishart and Wishart mixture model (WMM) classifiers, namely Wishart-CRF and WMM-CRF, to perform the classification. The model is compared with the Markov random field (MRF) based model as well as neural network-based models. The results are analysed in terms of accuracy and preservation of details such as edges and micro-regions. The model is assessed using three full polarimetric SAR benchmark data sets. The CRF model exhibits better classification results by significantly reducing the noise and preserving the finer details of edges and small regions.