Triple Temporal Vision Transformer for the Coverage Classification with Multi-Temporal Polsar Images

Multi-temporal SAR and Polarimetric SAR (PolSAR) images can provide the scattering change characteristics caused by vegetation growth to help the classifier capture phenological characteristics. To effectively utilize multi-temporal PolSAR data, a triple temporal vision transformer (TriTempoViT) model is proposed to capture correlation from multi-dimensional features. The method uses a three-branch network architecture to extract spatial-temporal, spatial-polarimetric, and temporal-polarization features respectively, and then the features from the three branches will be integrated into the vision transformer (ViT) for information interaction. Additionally, a 3D channel-spatial attention module (3DCSAM) is tailored to automatically weight the importance of the multi-dimensional feature maps. Moreover, a temporal interaction feature extraction module (TIFEM) is designed to comprehensively consider correlations between different temporal sequences. Compared with the recently developed state-of-the-art approach, the proposed method can improve OA of the classification accuracy in a Radarsat-2 dataset from Flevoland by about 0.84%, which proves the effectiveness of the proposed method.