关键词: 高光谱场景分类, 知识蒸馏, 中间层知识传递, 流形映射, Transformer

Abstract:

Most of the current scene classification tasks are based on high-resolution remote sensing images, and the lack of spectral information limits its discrimination ability for scene classification. While hyperspectral remote sensing images have the characteristic of “spatial-spectral integration”, which has unique advantages in scene classification. To address the issue of the complex landcover distribution and the high dimension and redundancy in hyperspectral images, this paper proposes a hyperspectral scene classification manifold distillation network (HSCMDNet) to improve the performance of hyperspectral remote sensing scene classification. For the complex landcover distribution of remote sensing images, HSCMDNet employs Swin Transformer as a teacher network to reveal the long-distance dependency information of hyperspectral images and capture the relationship between different bands. After that, a manifold distillation loss is designed in the middle layer of the teacher network and the student network ResNet-18. By matching the middle layer output features of the student model and the teacher model in the manifold space, the knowledge of the teacher model is transferred to the lightweight student model effectively, which alleviates the high computational complexity caused by high-dimensional hyperspectral data. In the orbita hyperspectral image scene classification dataset (OHID-SC) and natural scene classification with Tiangong-2 remotely sensed imagery (NaSC-TG2), the best classification accuracies of the proposed HSCMDNet network reached 93.60% and 94.55%, respectively.

Key words: hyperspectral scene classification, knowledge distillation, middle layer knowledge transfer, manifold mapping, Transformer