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Spectrally-Spatially Regularized Low-Rank and Sparse Decomposition: A Novel Method for Change Detection in Multitemporal Hyperspectral Images
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Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
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Remote Sens. 2017, 9(10), 1042; https://0-doi-org.brum.beds.ac.uk/10.3390/rs9101042
Received: 31 August 2017 / Revised: 5 October 2017 / Accepted: 10 October 2017 / Published: 12 October 2017
Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples. View Full-Text
Keywords: hyperspectral image (HSI); semi-supervised classification; generative adversarial networks (GANs); three-dimensional bilateral filter (3DBF) hyperspectral image (HSI); semi-supervised classification; generative adversarial networks (GANs); three-dimensional bilateral filter (3DBF)
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MDPI and ACS Style

He, Z.; Liu, H.; Wang, Y.; Hu, J. Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification. Remote Sens. 2017, 9, 1042. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9101042

AMA Style

He Z, Liu H, Wang Y, Hu J. Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification. Remote Sensing. 2017; 9(10):1042. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9101042

Chicago/Turabian Style

He, Zhi, Han Liu, Yiwen Wang, and Jie Hu. 2017. "Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification" Remote Sensing 9, no. 10: 1042. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9101042

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