Adaptive Neighborhood Strategy Based Generative Adversarial Network for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) is usually composed of hundreds of continuous bands, leading a challenge task for pixel-level classification owing to high-dimensional spectral features and insufficient labeled samples. In this paper, an adaptive neighborhood strategy based generative adversarial network with (AN-GAN) for semi-supervised HSI classification is proposed. The proposed AN-GAN approach firstly uses superpixel algorithm, e.g., simple linear iterative clustering (SLIC), to generate multiple spatially homogeneous regions. Furthermore, each superpixel is merged with its spectrally similar neighbor superpixels. Then, for the reconstructed superpixels, the limited labeled samples are used to train discriminator, and a large number of unlabeled samples are utilized to generate noise using sparse autoencoder and also used to train discriminator for purpose of improving discriminator performance. Experiments were conducted on both Pavia University and Indian Pines datasets, which show that AN-GAN could provide better classification performance comparing with state-of-the-art classification models.