Abstract
This paper introduces a new supervised classification method for hyperspectral images that combines spectral and spatial information. A support vector machine (SVM) classifier, integrated with a subspace projection method to address the problems of mixed pixels and noise, is first used to model the posterior distributions of the classes based on the spectral information. Then, the spatial information of the image pixels is modeled using an adaptive Markov random field (MRF) method. Finally, the maximum posterior probability classification is computed via the simulated annealing (SA) optimization algorithm. The combination of subspace-based SVMs and adaptive MRFs is the main contribution of this paper. The resulting methods, called SVMsub-eMRF and SVMsub-aMRF, were experimentally validated using two typical real hyperspectral data sets. The obtained results indicate that the proposed methods demonstrate superior performance compared with other classical hyperspectral image classification methods.
Highlights
In recent years, immense research efforts have been devoted to hyperspectral image classification.Given a set of observations, the goal of classification is to assign a unique label to each pixel vector such that it can be identified as belonging to a given class [1]
We performed experiments to compare the performances of two adaptive Markov random field (MRF) algorithms, the edge-constraint-based edge-constrained MRF method (eMRF) algorithm [17] and the relative homogeneity index (RHI)-based aMRF algorithm [18], and both of them achieved superior accuracies compared with other spectral-spatial hyperspectral image classifiers
Step 1: Compute the results of SVMsub according to Algorithm 1; Step 2: Obtain the first principal component using the minimum noise fraction (MNF) transform; Step 3: Detect the edges using the Canny or LoG detector and the results of Step 2; Step 4: Define the thresholds ρ1 and ρ2 to determine the β i using the results of Step 3 according to Equations (16) and (17); Step 5: Determine the final class labels y according to Equation (18); 3.3
Summary
Immense research efforts have been devoted to hyperspectral image classification. In [18], an adaptive MRF approach that uses a relative homogeneity index (RHI) to characterize the spatial contribution was proposed for the classification of hyperspectral imagery; this method is called aMRF. We performed experiments to compare the performances of two adaptive MRF algorithms, the edge-constraint-based eMRF algorithm [17] and the RHI-based aMRF algorithm [18], and both of them achieved superior accuracies compared with other spectral-spatial hyperspectral image classifiers In addition to these advantages, our approach provides a fast computation speed by virtue of the subspace-based.
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