Multilevel Feature Alignment Based on Spatial Attention Deformable Convolution for Cross-Scene Hyperspectral Image Classification

Abstract

Nowadays, domain adaptation (DA) is getting more attention in cross-scene hyperspectral image (HSI) classification, and various DA algorithms have been proposed. However, regular convolution indiscriminately extracting features around the center pixel will result in the inaccurate extraction of spatial-spectral features, which significantly affect the subsequent feature alignment. Meanwhile, the method of aligning the category features of source and target domains from a single-level may not cope well with complex HSIs. Therefore, we propose a multilevel feature alignment algorithm based on spatial attention deformable convolution (MFA-SADC), which achieves multilevel feature alignment from feature to feature, feature to cluster-center, and cluster-center to cluster-center. In addition, spatial attention deformable convolution is proposed to compose the feature extraction network of MFA-SADC, which guarantees the purity of spatial-spectral features. Experiments on three HSI datasets indicate MFA-SADC can obtain better classification performance when compared with the seven state-of-the-art methods.