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Higher Order Support Vector Random Fields for Hyperspectral Image Classification

1
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
2
International School, Beijing University of Posts and Telecommunications, Beijing 100876, China
3
Beijing Control and Electronic Technology Institute, Beijing 100038, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(1), 19; https://doi.org/10.3390/ijgi7010019
Received: 24 October 2017 / Revised: 19 December 2017 / Accepted: 6 January 2018 / Published: 11 January 2018
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. View Full-Text
Keywords: hyperspectral image classification; conditional random fields; support vector random fields; higher order potentials hyperspectral image classification; conditional random fields; support vector random fields; higher order potentials
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Yang, J.; Jiang, Z.; Hao, S.; Zhang, H. Higher Order Support Vector Random Fields for Hyperspectral Image Classification. ISPRS Int. J. Geo-Inf. 2018, 7, 19.

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ISPRS Int. J. Geo-Inf., EISSN 2220-9964, Published by MDPI AG
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