Abstract
Remote sensing (RS) technology is crucial for monitoring global changes, plays an important role in urban planning, disaster management, and environmental monitoring through building change detection (BCD). High‐resolution RS images used in BCD tasks face challenges such as overlooked derived information, complex backgrounds, sample imbalances, and the selection of an optimal learning rate, complicating their effective utilization. Consequently, the ACSPNet, a Siamese‐architecture BCD network, is introduced. Firstly, an adaptive edge visual feature extraction algorithm is designed to effectively capture architectural edge features, provide important a priori information, and reduce data redundancy and background noise problems. Secondly, coordinated context threshold‐awareness is proposed to enhance the convolutional feature representation through cross‐attention and threshold‐awareness strategies to improve the sensitivity of the model to discriminative features and effectively cope with complex background interference. Subsequently, the self‐calibrating visual field‐enhanced convolution is developed to expand the perceptual range of input features, significantly enhancing the detection of foreground information. This approach sharpens the network's focus on the foreground region and effectively addresses the issue of sample imbalance. Finally, a particle chameleon algorithm is designed to search for the optimal learning rate, thereby accelerating convergence and improving training efficiency. Comparative experiments highlight ACSPNet's superior performance over six state‐of‐the‐art BCD methods across the self‐built dataset (CSUFT‐CD) and three public datasets: Google‐CD, WHU‐CD, and LEVIR‐CD.