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CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing.

Li, Yinhe; Ren, Jinchang; Yan, Yijun; Liu, Qiaoyuan; Ma, Ping; Petrovski, Andrei; Sun, Haijiang


Yinhe Li

Jinchang Ren

Qiaoyuan Liu

Haijiang Sun


As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology.


LI, Y., REN, J., YAN, Y., LIU, Q., MA, P., PETROVSKI, A. and SUN, H. 2023. CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing. IEEE transactions on geoscience and remote sensing [online], 61, 5513011. Available from:

Journal Article Type Article
Acceptance Date May 11, 2023
Online Publication Date May 16, 2023
Publication Date Dec 31, 2023
Deposit Date May 25, 2023
Publicly Available Date May 25, 2023
Journal IEEE Transactions on Geoscience and Remote Sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 61
Article Number 5513011
Keywords Hyperspectral images (HSI); Change detection; Cross-band self-attention network (CBANet); Spatial-spectral feature extraction
Public URL


LI 2023 CBANet (AAM) (1.1 Mb)

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