This paper introduces a Channel Attention-based Bilateral Feature Pyramid U-Net (CABFPU-Net) for efficient change detection tasks in remote sensing. CABFPU-Net comprises three main networks: a backbone, a neck, and a head. The backbone, based on DenseNet, extracts primary features from input images. The neck network leverages channel attention to efficiently process multi-scale features, accentuating regions of change, and culminating in generation of multi-scale change attention features. This attention mechanism efficiently extracts relevant features by applying channel-wise attention, reducing dimensionality and enabling faster change detection. Finally, the head network integrates these features to produce a detailed change map. CABFPU-Net achieves a 53.9% reduction in processing time compared to CADNet on the LEVIR-CD dataset while maintaining F1-score of 91.3%, thereby demonstrating its efficiency and accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.