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arXiv · 2208.04481

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

Desen Meng·Feng Gao·Junyu Dong·Qian Du·Heng-Chao Li
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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

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Desen Meng, Feng Gao, Junyu Dong, Qian Du, Heng-Chao Li. 2022-08-09. Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network. https://doi.org/10.1109/lgrs.2022.3198088

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