PSCLI-TF: Position-Sensitive Cross-Layer Interactive Transformer Model for Remote Sensing Image Scene Classification
Daxiang Li, Runyuan Liu, Yao Tang, Ying Liu
Abstract
In the scene classification task of remote sensing image (RSI), in order to fully perceive multi-scale local objects in the image and explore their interdependencies to mine the scene semantics of RSI, this letter designs a novel Position-Sensitive Cross-Layer Interactive Transformer (PSCLI-TF) model to improve the accuracy of RSI scene classification. Firstly, ResNet50 is utilized as the backbone to extract the multi-layer feature maps of RSI. Then, in order to enhance the model’s position sensitivity to local objects in RSI, a new Position-Sensitive Cross-Layer Interactive Attention (PSCLIA) mechanism is designed, and based on it a novel PSCLI-TF encoder is constructed to perform layer-by-layer interactive fusion on the multi-layer feature maps to obtain the multi-granularity Cross-Layer Fusion (CLF) feature of RSI. Finally, a prototype-based self-supervised loss function is constructed to alleviate the semantic gap problem of "large intra-class variance and small inter-class variance" in RSI scene classification. Comparative experimental results based on three datasets (i.e., AID, NWPU and UCM) indicate that the classification performance of the designed PSCLI-TF model is highly competitive compared to other state-of-the-art methods.