Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network

In recent years, the deep neural network has shown a strong presence in classification tasks and its effectiveness has been well proved. However, the framework of DNN usually requires a large number of samples. Compared to the training sets in classification tasks, the training sets for the target d...

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Autores principales:	Yanlong Gao, Yan Feng, Xumin Yu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hyperspectral images target detection generative adversarial network Science Q
Acceso en línea:	https://doaj.org/article/84a7aae24c4041d7bdd46d2c7e54e0d2
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id	oai:doaj.org-article:84a7aae24c4041d7bdd46d2c7e54e0d2
record_format	dspace
spelling	oai:doaj.org-article:84a7aae24c4041d7bdd46d2c7e54e0d22021-11-11T18:57:34ZHyperspectral Target Detection with an Auxiliary Generative Adversarial Network10.3390/rs132144542072-4292https://doaj.org/article/84a7aae24c4041d7bdd46d2c7e54e0d22021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4454https://doaj.org/toc/2072-4292In recent years, the deep neural network has shown a strong presence in classification tasks and its effectiveness has been well proved. However, the framework of DNN usually requires a large number of samples. Compared to the training sets in classification tasks, the training sets for the target detection of hyperspectral images may only include a few target spectra which are quite limited and precious. The insufficient labeled samples make the DNN-based hyperspectral target detection task a challenging problem. To address this problem, we propose a hyperspectral target detection approach with an auxiliary generative adversarial network. Specifically, the training set is first expanded by generating simulated target spectra and background spectra using the generative adversarial network. Then, a classifier which is highly associated with the discriminator of the generative adversarial network is trained based on the real and the generated spectra. Finally, in order to further suppress the background, guided filters are utilized to improve the smoothness and robustness of the detection results. Experiments conducted on real hyperspectral images show the proposed approach is able to perform more efficiently and accurately compared to other target detection approaches.Yanlong GaoYan FengXumin YuMDPI AGarticlehyperspectral imagestarget detectiongenerative adversarial networkScienceQENRemote Sensing, Vol 13, Iss 4454, p 4454 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hyperspectral images target detection generative adversarial network Science Q
spellingShingle	hyperspectral images target detection generative adversarial network Science Q Yanlong Gao Yan Feng Xumin Yu Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network
description	In recent years, the deep neural network has shown a strong presence in classification tasks and its effectiveness has been well proved. However, the framework of DNN usually requires a large number of samples. Compared to the training sets in classification tasks, the training sets for the target detection of hyperspectral images may only include a few target spectra which are quite limited and precious. The insufficient labeled samples make the DNN-based hyperspectral target detection task a challenging problem. To address this problem, we propose a hyperspectral target detection approach with an auxiliary generative adversarial network. Specifically, the training set is first expanded by generating simulated target spectra and background spectra using the generative adversarial network. Then, a classifier which is highly associated with the discriminator of the generative adversarial network is trained based on the real and the generated spectra. Finally, in order to further suppress the background, guided filters are utilized to improve the smoothness and robustness of the detection results. Experiments conducted on real hyperspectral images show the proposed approach is able to perform more efficiently and accurately compared to other target detection approaches.
format	article
author	Yanlong Gao Yan Feng Xumin Yu
author_facet	Yanlong Gao Yan Feng Xumin Yu
author_sort	Yanlong Gao
title	Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network
title_short	Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network
title_full	Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network
title_fullStr	Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network
title_full_unstemmed	Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network
title_sort	hyperspectral target detection with an auxiliary generative adversarial network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/84a7aae24c4041d7bdd46d2c7e54e0d2
work_keys_str_mv	AT yanlonggao hyperspectraltargetdetectionwithanauxiliarygenerativeadversarialnetwork AT yanfeng hyperspectraltargetdetectionwithanauxiliarygenerativeadversarialnetwork AT xuminyu hyperspectraltargetdetectionwithanauxiliarygenerativeadversarialnetwork
_version_	1718431656734359552

Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network

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