  For hyperspectral image change detection (HSI-CD), one key challenge is to
reduce band redundancy, as only a few bands are crucial for change detection
while other bands may be adverse to it. However, most existing HSI-CD methods
directly extract change feature from full-dimensional HSIs, suffering from a
degradation of feature discrimination. To address this issue, we propose an
end-to-end hyperspectral image change detection network with band selection
(ECDBS), which effectively retains the critical bands to promote change
detection. The main ingredients of the network are a deep learning based band
selection module and cascading band-specific spatial attention (BSA) blocks.
The band selection module can be seamlessly integrated with subsequent CD
models for joint optimization and end-to-end reasoning, rather than as a step
separate from change detection. The BSA block extracts features from each band
using a tailored strategy. Unlike the typically used feature extraction
strategy that uniformly processes all bands, the BSA blocks considers the
differences in feature distributions among widely spaced bands, thereupon
extracting more sufficient change feature. Experimental evaluations conducted
on three widely used HSI-CD datasets demonstrate the effectiveness and
superiority of our proposed method over other state-of-the-art techniques.
