2022
DOI: 10.3390/rs14143394
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A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

Abstract: Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers’ attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the chan… Show more

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Cited by 5 publications
(1 citation statement)
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“…Li et al [51] combined the resnet-50 and the position attention module (PAM) to achieve high accuracy in the change detection of remote sensing images. Zhang et al [52] introduced a new spatial attention mechanism into the Siamese network, which also improved the accuracy of the model for detecting changes in multispectral images. Our research drew on these previous studies, and added the ECA attention mechanism after the backbone network of YOLOX, which improved the detection accuracy of the model.…”
Section: About the Backbone Networkmentioning
confidence: 99%
“…Li et al [51] combined the resnet-50 and the position attention module (PAM) to achieve high accuracy in the change detection of remote sensing images. Zhang et al [52] introduced a new spatial attention mechanism into the Siamese network, which also improved the accuracy of the model for detecting changes in multispectral images. Our research drew on these previous studies, and added the ECA attention mechanism after the backbone network of YOLOX, which improved the detection accuracy of the model.…”
Section: About the Backbone Networkmentioning
confidence: 99%