Prior Guidance and Principal Attention Network for Remote Sensing Image Change Detection

In the field of remote sensing (RS) image change detection (CD), the conventional encoder-decoder architecture networks often encounter three significant challenges. First, noise in the features extracted from traditional backbone networks leads to blurred boundaries of change objects. Second, upsampling techniques employed in the decoder, such as interpolation or deconvolution, are limited by their finite receptive fields, making it challenging to accurately distinguish pseudo-changes. Furthermore, how to merge encoder and decoder features with possible semantic gaps for the fine-grained details is a topic worth considering. To address these challenges, we introduce a prior guidance (PG) module that effectively aggregates prior high-level features as a semantic guidance map to guide encoder features for the enhancement of boundary detection. In addition, we design a principal attention (PA) module, which aggregates global information from principal regions through sparse operations and adaptively allocates this information to the upsampled and encoder features. This not only addresses the deficiency of global information in the upsampled features but also reduces the semantic gap between the encoder and decoder by establishing channel dependencies. PA does not divert attention to irrelevant regions, demonstrating excellent performance and computational efficiency. By integrating these two modules into our method, a novel PG and PA network (PGPANet) is elaborately designed. A wide range of experiments confirms the validity of our method, showcasing outstanding detection accuracy on three publicly available CD datasets: LEVIR-CD, SYSU-CD, and WHU-CD. The demo code of this work is publicly available at
https://github.com/DaGuangDaGuang/PGPANet
.