Cross-Scene Hyperspectral Image Classification based on Feature Learning.

Hyperspectral image classification provides land cover information for environment and urban management. However, one of the major challenges of hyperspectral image classification is the small amount of labeled data, which makes the training model have low accuracy and poor robustness. Therefore, this paper proposes a cross-scene classification model based on feature learning (CSCFL). Firstly, the model reduces the differences between different scenes based on unsupervised domain adaptation technology. Secondly, the depth separable convolution is introduced to improve the ability of the model to capture fine spatial features. Finally, the spherical tree is introduced for spatial segmentation to improve the search efficiency of weighted KNN (WKNN) classifier and save computing cost. Experimental results on Pavia and Indiana datasets show that the proposed algorithm has higher classification accuracy and lower computational cost.