Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net

Abstract

Hyperspectral image classification is a challenging task when a limited number of training samples are available. It is also known that the classification performance highly depends on the quality of the labeled samples. In this work, a cluster-based conditional generative adversarial net (CCGAN) is proposed as an effective solution to increase the size and quality of the training data set. The proposed method is able to automatically select the most representative initial samples with a subtractive clustering-based strategy, which keeps the diversity for sample generation. Moreover, compared to the traditional semisupervised classification frameworks, the CCGAN is able to generate realistic spectral profiles by considering the class-specific labels. Experiments on well-known Pavia University data set demonstrate that the proposed CCGAN can significantly boost the classification accuracy, even using a small number of initial labeled samples.