A Two-Stage Multiscale Network for High-Resolution Remote Sensing Images Change Detection

Deep learning-based change detection (CD) has emerged as a prominent research topic in the field of remote sensing (RS). However, existing deep learning-based CD methods still face challenges due to insufficient multiscale feature extraction and imprecise boundary delineation. To address the aforementioned problems, this article proposes a novel two-stage multiscale network (TSMSNet) for CD. In the encoding stage, a probability-gated multiscale convolutional modulation module (PGMCM) is proposed, which integrates convolutional modulation, strip convolution, and a gating mechanism. This module can comprehensively extract multiscale features from bitemporal RS images while significantly enhancing boundary information. To further refine multiscale feature extraction, a layer-decreasing pyramid pooling structure (LDPPS) is proposed within the skip connection architecture. During the decoding stage, a global-local relation-aware feature fusion (GLRAF) module is introduced to enhance the network’s capability in reconstructing image features. Finally, to optimize the learning process, a fuzzy-weighted binary cross-entropy loss function is designed to incorporate pixel classification uncertainty based on the degree of fuzziness. This novel loss function enables the network to focus more effectively on difficult-to-classify samples, thereby improving overall detection performance. The performance of the proposed network TSMSNet is validated on four publicly available CD datasets: WHU, GZ, GVLM, and HGG. TSMSNet achieves IoU/F1 scores of 83.07% /90.75%, 73.39% /84.65%, 79.65% /88.67%, and 75.58% /86.09% on these four datasets, respectively. Compared with six state-of-the-art methods, TSMSNet shows improvements of at least 5.03% /3.08%, 3.18% /2.15%, 1.69% /1.06%, and 1.75% /1.15% in terms of IoU and F1 score. These experimental results demonstrate the effectiveness of TSMSNet. The source code is available at https://github.com/xbddl/TSMSNet.