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Convolutional Two-Stream Generative Adversarial Network-Based Hyperspectral Feature Extraction
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DOI：10.1109/TGRS.2021.3073924.png)
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Abstract
En 中文
Hyperspectral image processing is faced with difficulties considering its redundant features and complex information. Studies on hyperspectral feature extraction in the deep learning domain have become increasingly popular. The mainstream techniques fully consider the spatial information in local neighborhoods when extracting spectral features by constructing deep neural networks. Deep generative models simulate the intrinsic structure of samples by adequately training, showing their potential values for signal processing. In this article, a convolutional two-stream network (cs(2)GAN-FE) based on the improved Wasserstein generative adversarial network (WGAN) is proposed for unsupervised hyperspectral spatial-spectral feature extraction. The improved WGAN is composed of one generator and one discriminator; the former perceives real data distributions, and the latter determines the attribution of generated data. The designed two-stream strategy is not a simple extension of a one-stream strategy and considers both the static spectral-spatial information and the dynamic spectral reflectance variation in multiple bands. Intrinsic spatial-spectral features are extracted by the trained discriminator considering sample distributions and feature relationships. The loss function is also improved for the unique structure of cs(2)GAN-FE. Various state-of-the-art techniques are chosen for comparison. Experimental results show the feasibility and potential of this network. Besides, experiments with the random split and the disjointed split both show that the proposed method can outperform other comparison techniques.
Keywords:
Feature extraction
Hyperspectral imaging
Generators
Generative adversarial networks
Streaming media
Shape
Data models
Feature extraction
generative model
hyperspectral image
spectral reflectance variance
unsupervised learning
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