Hyperspectral image classification with unsupervised feature extraction

Abstract

ABSTRACTDeep learning models have been widely applied to extract the high-level features of hyperspectral images (HSIs) due to their strong data mining ability, and some encouraging results have been obtained. Sufficient labelled samples are the guarantee of the network performance. However, the collection of labelled hyperspectral data is time consuming and labour consuming. To get rid of the limitation of labelled samples, an unsupervised feature extractor is proposed based on transfer learning and an improved Wasserstein generative adversarial network (WGAN-GP) in this paper. In addition, considering the spectral and spatial characteristics of HSIs, the framework is designed in three-dimensional (3D) form which helps fully exploit the information in hyperspectral data. Thanks to transfer learning, the well-trained discriminator of WGAN-GP is used as a feature extractor for learning the spectral-spatial features without involving labelled samples. Moreover, in order to facilitate the optimization of WGAN-GP, we propose a novel dimensionality reduction (DR) method using convolutions to get lower-dimensional target data. Experimental results on real hyperspectral datasets demonstrate the efficiency of the proposed method combining the proposed DR and WGAN-GP(DR+WGAN-GP), which has great potential prospects in HSI classification.