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High-resolution remote sensing image change detection focuses on ground surface changes. It has wide applications, including territorial spatial planning, urban region detection, and military operations. However, class imbalance and pseudo-changes are caused by the unchanged areas far outnumbering the changed areas and lighting changes. To address these problems, we propose spatial-temporal attention with a difference enhancement-based network (STADE-CDNet). In STADE-CDNet, a change detection difference enhancement module (CDDM) is proposed to extract important features from the difference map to detect changed regions. This module enhances the network with differential feature attributes through the training layer, improving the network’s learning ability and reducing the imbalance problem. A temporal memory module (TMM) is designed to extract temporal and spatial information. Inspired by the self-attention mechanism of the transformer, we propose a transformer and TMM (TTMM). Four encoding layers are designed to detect the semantic information from high to low levels of the multitemporal image pairs. The fusion and parallelism of multivariate data are achieved through collaborative modeling of deep learning and change detection, compensating for the need for excessive human intervention in traditional algorithms. We evaluate our approach in two different datasets (LEVIR-CD and DSIFN-CD). Promising quantitative and qualitative results show that STADE-CDNet can improve accuracy. In particular, the proposed CDDM significantly reduces false positive detection, with F1 scores at least 1.97% and 2.1% higher than other methods in the case of the LEVIR-CD and DSIFN-CD datasets, respectively. Our code is available at https://github.com/LiLisaZhi/STADE-CDNet.
Li et al. (Mon,) studied this question.