Multi-Label Hyperspectral Classification with Discriminative Features

For hyperspectral classification, mixed pixels are usually the biggest reason to reduce the classification accuracy. To solve the problem, we apply the multi-label classification technique to the hyperspectral image classification. The approach Label-specific Features (LIFT) achieves state-of-the-art performance because the most distinctive features are constructed for each label. Clustering centers play an essential role in label-specific feature conversion. However, LIFT does not consider the relationship between positive and negative instances, and clustering centers on the training dataset is inconsistent with that of the original instances. In this paper, we propose a new algorithm called SMOTE_DFL, which can get better clustering centers through two strategies: 1) The spectral clustering algorithm SIA is introduced to focus on the local structure between positive and negative instances; 2) By oversampling training sample, the clustering center of training sample is close to that of the whole. Extensive experiments are conducted on three datasets. The results validate the superiority of SMOTE_DFL to other algorithms.