PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field

TL;DR

This paper introduces a robust PolSAR image classification method that combines low-rank feature extraction, CNN-based classification, and Markov random field smoothing to effectively handle speckle noise and improve spatial consistency.

Contribution

It proposes a novel combination of Gaussian-based low-rank matrix factorization, CNN classification with data augmentation, and MRF refinement for enhanced PolSAR image classification.

Findings

Achieves promising classification accuracy on benchmark datasets.

Improves spatial consistency of classification maps.

Effectively suppresses speckle noise in PolSAR images.

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task nowadays. One significant barrier lies in the speckle effect embedded in the PolSAR imaging process, which greatly degrades the quality of the images and further complicates the classification. To this end, we present a novel PolSAR image classification method, which removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via Markov random field (MRF). Specifically, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises. Then, a classification map is obtained by applying convolutional neural network with data augmentation on the extracted features, where local consistency is implicitly involved, and the insufficient label issue is alleviated. Finally, we refine the classification map by MRF to enforce contextual smoothness. We conduct experiments on two benchmark PolSAR datasets. Experimental results indicate that the proposed method achieves promising classification performance and preferable spatial consistency.