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Research output: Contribution to Journal/Magazine › Journal article › peer-review
SSA-SiamNet : Spectral-Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection. / Wang, L.; Wang, Liguo; Wang, Q. et al.
In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, 5510018, 31.01.2022.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - SSA-SiamNet
T2 - Spectral-Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection
AU - Wang, L.
AU - Wang, Liguo
AU - Wang, Q.
AU - Atkinson, P.M.
N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2022/1/31
Y1 - 2022/1/31
N2 - 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.
AB - 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.
KW - Attention mechanism
KW - change detection (CD)
KW - Convolution
KW - convolutional block attention module (CBAM)
KW - Feature extraction
KW - hyperspectral images (HSIs)
KW - Hyperspectral imaging
KW - Redundancy
KW - Siamese network (SiamNet).
KW - Task analysis
KW - Tensors
KW - Training
KW - Deep learning
KW - Learning systems
KW - Spectroscopy
KW - Change detection
KW - Image change detection
KW - Learning methods
KW - Network training
KW - Spatial features
KW - Spatial location
KW - Spectral channels
KW - Unchanged pixels
KW - Convolutional neural networks
U2 - 10.1109/TGRS.2021.3095899
DO - 10.1109/TGRS.2021.3095899
M3 - Journal article
VL - 60
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
M1 - 5510018
ER -