Binary Change Detection of Remote Sensing Images with Deformable Convolution

Binary change detection (BCD) in remote sensing images is essential for tracking environmental and urban transformations, yet it grapples with challenges like scene complexity, multi-scale variations, and pseudo-changes due to illumination or seasonal shifts. Conventional convolutional neural networks(CNNs) are limited by fixed receptive fields. This limitation hinders their ability to effectively capture multi-scale variations and irregular shapes prevalent in remote sensing imagery. To overcome these constraints, we introduce DCNet that leverages deformable convolution for adaptive feature extraction. DCNet features a hierarchical encoder with deformable convolution and an intertemporal feature interaction module to mitigate pseudo-changes. By dynamically adjusting sampling locations, DCNet captures multi-scale and irregular changes, achieving state-of-the-art performance. Evaluations on the LEVIR-CD and WHU-CD datasets show DCNet surpassing state-of-the-art methods, with IoU improvements of 0.54% and 2.29%, respectively.