EfficientCD: A New Strategy for Change Detection Based With Bi-Temporal Layers Exchanged

Abstract

With the widespread application of remote sensing (RS) technology in environmental monitoring, the demand for efficient and accurate RS image change detection (CD) for natural environments is growing. We propose a novel deep learning framework named EfficientCD, specifically designed for RS image CD. The framework employs EfficientNet as its backbone network for feature extraction. To enhance the information exchange between bi-temporal image feature maps, we have designed a new feature pyramid network module targeted at RS CD, named ChangeFPN. In addition, to make full use of the multilevel feature maps in the decoding stage, we have developed a layer-by-layer feature upsampling module combined with Euclidean distance to improve feature fusion and reconstruction during the decoding stage. The EfficientCD has been experimentally validated on four RS datasets: LEVIR-CD, SYSU-CD, CLCD, and WHUCD. The experimental results demonstrate that EfficientCD exhibits outstanding performance in CD accuracy. The code and pretrained models will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dyzy41/mmrscd</uri>.