Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network
This work addresses change detection in SAR images, which is important for remote sensing applications, but it appears incremental as it builds on existing CNN-based approaches with specific improvements.
The paper tackles the problem of change detection in synthetic aperture radar (SAR) images by addressing issues with existing CNN-based methods, such as neglecting interactions among multilayer convolutions and errors from preclassification, resulting in a proposed network (LANTNet) that outperforms state-of-the-art methods on three datasets.
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