Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information
In this paper, we introduce a novel classification framework for hyperspectral images (HSIs) by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specific...
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doaj-2fd962fd054d48f0b5611c461da9e61f2020-11-24T21:54:46ZengMDPI AGRemote Sensing2072-42922018-03-0110344110.3390/rs10030441rs10030441Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure InformationYi Wang0Hexiang Duan1Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaInstitute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaIn this paper, we introduce a novel classification framework for hyperspectral images (HSIs) by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS), marker-based hierarchical segmentation (MHSEG) and algebraic multigrid (AMG) are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy.http://www.mdpi.com/2072-4292/10/3/441hyperspectral images (HSIs)classificationSVMcomposite kernelalgebraic multigrid methods |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Wang Hexiang Duan |
spellingShingle |
Yi Wang Hexiang Duan Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information Remote Sensing hyperspectral images (HSIs) classification SVM composite kernel algebraic multigrid methods |
author_facet |
Yi Wang Hexiang Duan |
author_sort |
Yi Wang |
title |
Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information |
title_short |
Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information |
title_full |
Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information |
title_fullStr |
Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information |
title_full_unstemmed |
Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information |
title_sort |
classification of hyperspectral images by svm using a composite kernel by employing spectral, spatial and hierarchical structure information |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2018-03-01 |
description |
In this paper, we introduce a novel classification framework for hyperspectral images (HSIs) by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS), marker-based hierarchical segmentation (MHSEG) and algebraic multigrid (AMG) are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy. |
topic |
hyperspectral images (HSIs) classification SVM composite kernel algebraic multigrid methods |
url |
http://www.mdpi.com/2072-4292/10/3/441 |
work_keys_str_mv |
AT yiwang classificationofhyperspectralimagesbysvmusingacompositekernelbyemployingspectralspatialandhierarchicalstructureinformation AT hexiangduan classificationofhyperspectralimagesbysvmusingacompositekernelbyemployingspectralspatialandhierarchicalstructureinformation |
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