Abstract
This paper addresses the problem of contextual hyperspectral image (HSI) classification. A novel conditional random fields (CRFs) model, known as higher order support vector random fields (HSVRFs), is proposed for HSI classification. By incorporating higher order potentials into a support vector random fields with a Mahalanobis distance boundary constraint (SVRFMC) model, the HSVRFs model not only takes advantage of the support vector machine (SVM) classifier and the Mahalanobis distance boundary constraint, but can also capture higher level contextual information to depict complicated details in HSI. The higher order potentials are defined on image segments, which are created by a fast unsupervised over-segmentation algorithm. The higher order potentials consider the spectral vectors of each of the segment’s constituting pixels coherently, and weight these pixels with the output probability of the support vector machine (SVM) classifier in our framework. Therefore, the higher order potentials can model higher-level contextual information, which is useful for the description of challenging complex structures and boundaries in HSI. Experimental results on two publicly available HSI datasets show that the HSVRFs model outperforms traditional and state-of-the art methods in HSI classification, especially for datasets containing complicated details.
Highlights
With the development of hyperspectral imaging technology, hyperspectral image (HSI) classification has attracted increasing attention in various fields such as disaster monitoring, precision agriculture, and the military
We propose a novel model known as higher order support vector random fields (HSVRFs), which incorporates higher order potentials into the SVRFMC model, for HSI classification
Experimental results show that the HSVRFs model outperforms traditional and state-of-the-art methods for HSI classification, and its advantages are more obvious for HSI with high spatial resolution and more complicated details
Summary
With the development of hyperspectral imaging technology, hyperspectral image (HSI) classification has attracted increasing attention in various fields such as disaster monitoring, precision agriculture, and the military. Li et al [23] implemented multi-class object segmentation using superpixel-based CRFs. Kohli et al [24,25] proposed adding robust higher order potential to pairwise CRFs to enforce label consistency in image labeling tasks. By integrating the higher order potentials into SVRFMC model, the HSVRFs model takes advantage of the SVM classifier and the pairwise potential of Mahalanobis distance boundary constraint, but can capture higher level context in HSI. We propose a new conditional random field model named HSVRFs to exploit both spectral and spatial information and their context for HSI classification based on the SVM classifier, the Mahalanobis distance boundary constraint model, and higher order potentials.
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