Semantic-Explicit Filtering Network for Remote Sensing Image Change Detection

Remote sensing image change detection (RSI-CD) aims to explore surface change information from aligned dual-phase images. However, RSI-CD currently encounters two major challenges. The first issue is the inadequate object-level semantic representation during the feature extraction in CD networks. The other issue is the spectral resolution of the RS image is limited, which leads to a mixture of pseudochange and real change. In order to explore the above-mentioned two challenges, we propose a semantic-explicit filtering network (SFNet) based on a neighborhood feature attention module (NFAM) and multiple-receptive-field semantic filtering mechanism (MSFM). First, the NFAM exploits the correlation of multiscale features and fuses features from the proximity layer to enhance the semantic-explicit representation of the object level. Then, the MSFM takes the weight map after the enhanced semantic representation as input and progressively refines the weight map through a multiple-receptive-field parallel convolution (MPC). This process filters out pseudochange from the predicted result while retaining the real-change information. The experiments on two benchmark datasets demonstrate that the proposed approach presents satisfactory performance over the existing methods.