A Deep Supervised Change Detection Network Based on Context-Rich Information

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Yanni Zhang, Lei Yang, Licai Zhu, Caigen Zhou, Yaser Ahangari Nanehkaran, Jianzhong Wang

Published: 2025, Last Modified: 10 Nov 2025IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: With the rapid advancement of deep learning techniques, convolutional neural network (CNN)-based change detection (CD) methods have achieved remarkable progress in remote sensing image analysis, primarily attributed to their exceptional ability to extract localized spatial features. However, these methods remain constrained by two fundamental limitations: first, restricted receptive field that hinder comprehensive contextual understanding, and second inadequate capacity for capturing long-range global dependencies. To address these challenges, we propose a deep supervised change detection network based on context-rich information (DSCRNet) featuring three innovative components: a CNN TransConv Block (CTCB), a hybrid dual-attention block (HDAB), and a multiscale-supervised module (MSM). The CTCB module synergistically integrates lightweight convolutional operations with Transformer architectures through a novel dual-branch framework. This hybrid design not only enhances CNN’s ability in local feature extraction but also leverages Transformer’s self-attention mechanism to effectively model global contextual relationships. The HDAB module enhances discriminative feature learning through two key innovations: first, spatial–channel transformation operations enabling cross-scale feature interaction, and second, a parallel attention mechanism combining channel-wise and spatial attention to produce more discriminative feature representations. The MSM introduces a parameter-free multiscale supervision mechanism that enables progressive refinement of change features through hierarchical feature fusion while maintaining computational efficiency. The experimental results and ablation studies conducted on the LEVIR-CD, CDD, and DSIFN-CD datasets indicate that the proposed DSCRNet achieves superior performance than the existing methods, attributed to the integration of the CTCB and other key modules.