Remote sensing image small target and moving target change detection method

Multi-temporal remote sensing image change detection is an important research direction in the field of remote sensing, which has a wide range of applications in environmental monitoring, urban change, agricultural investigation, war symptoms prediction, military force deployment, military strike damage assessment and so on. In order to solve the difficult problem of change detection of some moving objects and small objects in remote sensing images, it is necessary to mine the global complex semantic information in the feature extraction stage, and the current research on this aspect is not in-depth. In this paper, a 3-branch TSPNet network based on Transformer structure and SKAttention mechanism is proposed. This model has advantages of high detection accuracy, full mining of global semantic information and high degree of refinement, which can realize fine detection and moving target detection. The model was tested on the CDD and Levi-CD datasets, and parameters such as F1 scores and mIoU outperformed other mainstream networks.