Sample Iterative Enhancement Approach for Improving Classification Performance of Hyperspectral Imagery

Zhiyong Lv, Pengfei Zhang, Weiwei Sun, Tao Lei, Jón Atli Benediktsson, Peng Li

Abstract: Supervised classification with hyperspectral remote-sensing images (HRSIs) plays an important role in practical applications. However, labeling samples with HRSIs for supervised classification is time-consuming and labor-intensive. In this letter, we propose a new sample enhancement approach to improve the classification performance of HRSIs. First, the uncertainty and representativeness of the sample are defined to achieve sample possibility measurement for each pixel, and some pixels with high possibility can be selected as candidate samples. Then, two rules related to label correlation analysis and spectral similarity are defined to further refine the candidate samples used for generating the final sample set. Finally, the above-mentioned steps are fused into an iterative algorithm to enhance and balance the training samples for each class. The feasibility of the proposed approach was verified by applying it to classification with two real HRSIs. A comparison with some typical traditional sample enhancement methods and widely used few-shot deep-learning methods indicated the advantages of the proposed approach for improving classification accuracies. The improvement achieved by our proposed approach is about 0.79% ~ 2.31% in terms of the overall accuracy (OA). The code of the proposed approach is available at https://github.com/ImgSciGroup/2023-GRSL-SIEA.