Abstract
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.
Highlights
With the rapid development of hyperspectral sensors, the present hyperspectral images (HSIs) contain rich spectral and spatial information
We reported the classification results in the case of M = 40 in Table 2 to show the contribution of each kernel in the proposed method with μSPE = 0.3, μSPA = 0.1 and μHIE = 0.6 in KSPE−SPA−HIE, τ = 1, σ = 0.1, υ = 0.3, β = 0.01 and S = 11 were used by the algebraic multigrid (AMG)-marker-based hierarchical segmentation (MHSEG) algorithm, and the principal components (PCs) 1-3 and n = 8 were used for the constructions of the extended morphological profiles (EMPs)
The improvement of KSPE−SPA−HIE over the other kernels in Table 1 demonstrates that the combination of the spectral, spatial, and hierarchical kernels can generate better classification results than using a single or double kernels in terms of overall accuracy (OA), AA, and κ
Summary
With the rapid development of hyperspectral sensors, the present hyperspectral images (HSIs) contain rich spectral and spatial information. HSIs are often corrupted with different types of noise and dominated by mixed pixels. To solve these problems, many researchers recurred to pixel-wise methods to classify each pixel in HSIs to a certain class using its spectral information individually [9,10,11,12,13,14]. The SVM [10,15] and multinomial logistic regression (MLR) [16,17,18] are the two most commonly used techniques These methods often result in much “salt-and-pepper” noise in classification maps, without considering spatial neighborhoods, and the classification performance cannot be further improved
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