Unsupervised feature extraction based on improved Wasserstein generative adversarial network for hyperspectral classification

Qiaoqiao Sun; Salah Bourennane

doi:10.1117/12.2527466

21 June 2019 Unsupervised feature extraction based on improved Wasserstein generative adversarial network for hyperspectral classification

Qiaoqiao Sun, Salah Bourennane

Author Affiliations +

Proceedings Volume 11059, Multimodal Sensing: Technologies and Applications; 110590L (2019) https://doi.org/10.1117/12.2527466
Event: SPIE Optical Metrology, 2019, Munich, Germany

Abstract

Accurate classification is one of the most important prerequisites for hyperspectral applications and feature extraction is the key step of classification. Recently, deep learning models have been successfully used to extract the spectral-spatial features in hyperspectral images (HSIs). However, most deep learning-based classification methods are supervised and require sufficient samples to guarantee their performance. And the labeled samples in HSI are limited. To solve this problem, unsupervised feature extraction based on improved Wasserstein generative adversarial network (WGAN) is proposed in this paper. Further, in order to fully explore the spectral-spatial features in HSIs, a three-dimensional (3D) model is designed. Considering that its difficult for GANs to generate high dimension data, the dimension of HSI is reduced firstly by principle component analysis (PCA). Then, an improved WGAN is trained unsupervised with HSIs after dimensionality reduction to achieve the stable status. Finally, the discriminator of the trained improved WGAN is used as a feature extractor and followed by a classifier, which can be used to classify the HSIs. In order to evaluate the performance of the proposed method, PCA-based methods and GAN-based methods are compared in the experiment of a real-world HSI. Experimental results have shown that our proposed method gained more promising results which has great potential prospects in HSI classification.

Conference Presentation

Citation Download Citation

Qiaoqiao Sun and Salah Bourennane "Unsupervised feature extraction based on improved Wasserstein generative adversarial network for hyperspectral classification", Proc. SPIE 11059, Multimodal Sensing: Technologies and Applications, 110590L (21 June 2019); https://doi.org/10.1117/12.2527466

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