Sea-Land Clutter Classification Based on Graph Spectrum Features

In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset...

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Autores principales:	Le Zhang, Anke Xue, Xiaodong Zhao, Shuwen Xu, Kecheng Mao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	radar clutter classification graph feature extraction support vector machine average amplitude level Science Q
Acceso en línea:	https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf10
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id	oai:doaj.org-article:c229db41ccb64d25b7c8298feedcaf10
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spelling	oai:doaj.org-article:c229db41ccb64d25b7c8298feedcaf102021-11-25T18:54:34ZSea-Land Clutter Classification Based on Graph Spectrum Features10.3390/rs132245882072-4292https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf102021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4588https://doaj.org/toc/2072-4292In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.Le ZhangAnke XueXiaodong ZhaoShuwen XuKecheng MaoMDPI AGarticleradar clutter classificationgraph feature extractionsupport vector machineaverage amplitude levelScienceQENRemote Sensing, Vol 13, Iss 4588, p 4588 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	radar clutter classification graph feature extraction support vector machine average amplitude level Science Q
spellingShingle	radar clutter classification graph feature extraction support vector machine average amplitude level Science Q Le Zhang Anke Xue Xiaodong Zhao Shuwen Xu Kecheng Mao Sea-Land Clutter Classification Based on Graph Spectrum Features
description	In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.
format	article
author	Le Zhang Anke Xue Xiaodong Zhao Shuwen Xu Kecheng Mao
author_facet	Le Zhang Anke Xue Xiaodong Zhao Shuwen Xu Kecheng Mao
author_sort	Le Zhang
title	Sea-Land Clutter Classification Based on Graph Spectrum Features
title_short	Sea-Land Clutter Classification Based on Graph Spectrum Features
title_full	Sea-Land Clutter Classification Based on Graph Spectrum Features
title_fullStr	Sea-Land Clutter Classification Based on Graph Spectrum Features
title_full_unstemmed	Sea-Land Clutter Classification Based on Graph Spectrum Features
title_sort	sea-land clutter classification based on graph spectrum features
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf10
work_keys_str_mv	AT lezhang sealandclutterclassificationbasedongraphspectrumfeatures AT ankexue sealandclutterclassificationbasedongraphspectrumfeatures AT xiaodongzhao sealandclutterclassificationbasedongraphspectrumfeatures AT shuwenxu sealandclutterclassificationbasedongraphspectrumfeatures AT kechengmao sealandclutterclassificationbasedongraphspectrumfeatures
_version_	1718410617584353280

Sea-Land Clutter Classification Based on Graph Spectrum Features

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