Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network

TL;DR

This paper introduces LANTNet, a novel SAR change detection network that uses layer attention and a noise-tolerant loss to improve robustness against noisy labels and enhance detection accuracy.

Contribution

The paper proposes a layer attention module and a noise-tolerant loss function, addressing limitations of existing CNN-based SAR change detection methods.

Findings

LANTNet outperforms several state-of-the-art methods on three SAR datasets.

The layer attention mechanism effectively weights features from different convolution layers.

The noise-tolerant loss reduces the impact of noisy labels in preclassification.

Abstract

Recently, change detection methods for synthetic aperture radar (SAR) images based on convolutional neural networks (CNN) have gained increasing research attention. However, existing CNN-based methods neglect the interactions among multilayer convolutions, and errors involved in the preclassification restrict the network optimization. To this end, we proposed a layer attention-based noise-tolerant network, termed LANTNet. In particular, we design a layer attention module that adaptively weights the feature of different convolution layers. In addition, we design a noise-tolerant loss function that effectively suppresses the impact of noisy labels. Therefore, the model is insensitive to noisy labels in the preclassification results. The experimental results on three SAR datasets show that the proposed LANTNet performs better compared to several state-of-the-art methods. The source codes are available at https://github.com/summitgao/LANTNet