Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network

TL;DR

This paper introduces DPDNet, a dual path denoising network that improves SAR image change detection by reducing training time and label noise, achieving superior results on multiple datasets.

Contribution

The paper proposes a novel dual path denoising network with random label propagation and distinctive patch convolution for efficient SAR change detection.

Findings

Outperforms state-of-the-art methods in change detection accuracy

Reduces training time and label noise impact

Effective on five SAR datasets

Abstract

Benefited from the rapid and sustainable development of synthetic aperture radar (SAR) sensors, change detection from SAR images has received increasing attentions over the past few years. Existing unsupervised deep learning-based methods have made great efforts to exploit robust feature representations, but they consume much time to optimize parameters. Besides, these methods use clustering to obtain pseudo-labels for training, and the pseudo-labeled samples often involve errors, which can be considered as "label noise". To address these issues, we propose a Dual Path Denoising Network (DPDNet) for SAR image change detection. In particular, we introduce the random label propagation to clean the label noise involved in preclassification. We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption. Specifically, the attention mechanism is used to select distinctive pixels in the feature maps, and patches around these pixels are selected as convolution kernels. Consequently, the DPDNet does not require a great number of training samples for parameter optimization, and its computational efficiency is greatly enhanced. Extensive experiments have been conducted on five SAR datasets to verify the proposed DPDNet. The experimental results demonstrate that our method outperforms several state-of-the-art methods in change detection results.