Polarimetric synthetic aperture radar image classification for accurate landcover mapping with advanced encoder–decoder networks using multi-dimensional probabilistic voting ensemble

Abstract

Polarimetric synthetic aperture radar (PolSAR) is an important technology in radar remote sensing. It can capture complex, multidimensional data on the Earth's surface. This technology is essential for detailed terrain analysis. It enables the differentiation of various land covers by examining scattering patterns. We introduce the multi-dimensional probabilistic voting ensemble network (MD-PVE-Net). It is a semi-supervised framework that combines multiple polarimetric decomposition algorithms with advanced convolutional neural network (CNN) architectures, notably encoder-decoder architectures such as U-Net and residual U-Net. These architectures are designed to maintain spatial relationships and generate dense output maps, addressing the challenges of pixel-wise classification present in contemporary CNN-based models. The incorporation of various decomposition techniques, including Cloude, Huynen, HAAlpha, Freeman-Durden, Vanzyl, and Yamaguchisignificantly enhances the feature extraction process, providing detailed statistical descriptions of scattering mechanisms. MD-PVE-Net leverages these improved features to substantially increase labels' accuracy for unlabeled data, thus enriching the training dataset. Empirical validation using three distinct PolSAR datasets shows that MD-PVE-Net surpasses current classification methods, especially in complex agricultural environments where accurate crop type discrimination is essential. The ResU-Net architecture, with its deep layers and integration of residual blocks, helps process the intricate, high-dimensional PolSAR data, achieving superior classification results compared with other classification methods. An additional PolSAR dataset is used to validate and confirm the effectiveness of the proposed models in real-world scenarios.