Abstract
Hyperspectral images (HSIs) have been widely used in remote sensing change detection for environment monitoring and urban studies. Aiming to leverage the rich information of HSIs, we proposed a joint spatial-spectral-temporal attention method for HS image change detection in this paper. To this end, we put together the convolutional block attention module (CBAM) and the recurrent neural network (RNN) to the end-to-end network. The CBAM can get spatial and spectral feature representation, and the latter can use the temporal information by a long short-term memory. Experiments on one HSIs change detection dataset demonstrated that our proposed method could get effective performance in HSIs for change detection.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
