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.
Highlights
Hyperspectral imaging systems can acquire numerous contiguous spectral bands throughout the electromagnetic spectrum
Many segmentation techniques have been proposed, including watershed, partitional clustering, the hierarchical segmentation (HSEG) algorithm, and minimum spanning forest (MSF), to segment hyperspectral imagery into homogeneous regions according to a homogeneity criterion
The marker selection methods are based on the performance of pixel-wise classifiers, i.e., the performance of different pixel-wise classifiers leads to different markers and uncertainties in the classification results
Summary
Hyperspectral imaging systems can acquire numerous contiguous spectral bands throughout the electromagnetic spectrum. The automatic segmentation of hyperspectral imagery is still an continuing problem To remedy this problem, a marker-controlled segmentation method was proposed to automatically select a single hierarchical segmentation level [24]. A marker-controlled segmentation method was proposed to automatically select a single hierarchical segmentation level [24] The idea behind this approach is to select at least one pixel for each spatial object and to grow regions from the selected seeds ( called markers) to guarantee that each region is associated with one marker in the segmentation maps. The experimental results in [24,25] demonstrated that the classification accuracies of the HSEG algorithm and MSF segmentation algorithms using automatically selected markers can greatly outperform the SVM classifier. The randomness of the training samples in the pixel-wise classification procedure always generates stochastic markers, which results in unstable classification accuracies
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