SSA-SiamNet : Spectral-Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection

Wang, L. and Wang, Liguo and Wang, Q. and Atkinson, P.M. (2022) SSA-SiamNet : Spectral-Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5510018. ISSN 0196-2892

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Deep learning methods, especially convolutional neural network (CNN)-based methods, have shown promising performance for hyperspectral image (HSI) change detection (CD). It is acknowledged widely that different spectral channels and spatial locations in input image patches may contribute differently to CD. However, they are treated equally in existing CNN-based approaches. To increase the accuracy of HSI CD, we propose an end-to-end Siamese CNN (SiamNet) with a spectral-spatial-wise attention (SSA-SiamNet) mechanism. The proposed SSA-SiamNet method can emphasize informative channels and locations and suppress less informative ones to refine the spectral-spatial features adaptively. Moreover, in the network training phase, the weighted contrastive loss function is used for more reliable separation of changed and unchanged pixels and to accelerate the convergence of the network. SSA-SiamNet was validated using four groups of bitemporal HSIs. The accuracy of CD using the SSA-SiamNet was found to be consistently greater than for ten benchmark methods.

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Journal Article
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IEEE Transactions on Geoscience and Remote Sensing
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Uncontrolled Keywords:
?? attention mechanismchange detection (cd)convolutionconvolutional block attention module (cbam)feature extractionhyperspectral images (hsis)hyperspectral imagingredundancysiamese network (siamnet).task analysistensorstrainingdeep learninglearning systemssp ??
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03 Sep 2021 11:40
Last Modified:
12 Feb 2024 00:41