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Remote Sens. 2016, 8(4), 296; doi:10.3390/rs8040296

A Spectral-Spatial Classification of Hyperspectral Images Based on the Algebraic Multigrid Method and Hierarchical Segmentation Algorithm

1,2
and
1,2,*
1
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
2
Subsurface Multi-Scale Imaging Laboratory of Hubei Province (SMIL), China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 31 December 2015 / Revised: 2 March 2016 / Accepted: 21 March 2016 / Published: 31 March 2016
View Full-Text   |   Download PDF [8949 KB, uploaded 31 March 2016]   |  

Abstract

The algebraic multigrid (AMG) method is used to solve linear systems of equations on a series of progressively coarser grids and has recently attracted significant attention for image segmentation due to its high efficiency and robustness. In this paper, a novel spectral-spatial classification method for hyperspectral images based on the AMG method and hierarchical segmentation (HSEG) algorithm is proposed. Our method consists of the following steps. First, the AMG method is applied to hyperspectral imagery to construct a multigrid structure of fine-to-coarse grids based on the anisotropic diffusion partial differential equation (PDE). The vertices in the multigrid structure are then considered as the initial seeds (markers) for growing regions and are clustered to obtain a sequence of segmentation results. In the next step, a maximum vote decision rule is employed to combine the pixel-wise classification map and the segmentation maps. Finally, a final classification map is produced by choosing the optimal grid level to extract representative spectra. Experiments based on three different types of real hyperspectral datasets with different resolutions and contexts demonstrate that our method can obtain 3.84%–13.81% higher overall accuracies than the SVM classifier. The performance of our method was further compared to several marker-based spectral-spatial classification methods using objective quantitative measures and a visual qualitative evaluation. View Full-Text
Keywords: algebraic multigrid methods; classification; hyperspectral images; marker selection; spectral-spatial; hierarchical segmentation algebraic multigrid methods; classification; hyperspectral images; marker selection; spectral-spatial; hierarchical segmentation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Song, H.; Wang, Y. A Spectral-Spatial Classification of Hyperspectral Images Based on the Algebraic Multigrid Method and Hierarchical Segmentation Algorithm. Remote Sens. 2016, 8, 296.

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