Polarimetric SAR Image Semantic Segmentation with 3D Discrete Wavelet Transform and Markov Random Field

TL;DR

This paper introduces a novel PolSAR image segmentation method combining 3D discrete wavelet transform and Markov random field to improve accuracy and spatial consistency, especially with limited labeled data.

Contribution

It is the first to integrate 3D-DWT features with MRF priors for PolSAR segmentation, enhancing robustness and contextual information utilization.

Findings

Achieves high segmentation accuracy on benchmark datasets.

Ensures smooth and consistent segmentation results.

Requires minimal labeled pixels for effective performance.

Abstract

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper.With a newly defined channelwise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.