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Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields

Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields


Title: Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields
Author: Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Úlfarsson, Magnús Örn
Ghamisi, Pedram
Date: 2014
Language: English
Scope: 2565-2574
University/Institute: Háskóli Íslands
University of Iceland
School: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Department: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
Series: IEEE Transactions on Geoscience and Remote Sensing;52(5)
ISSN: 0196-2892
1558-0644 (e-ISSN)
DOI: 10.1109/TGRS.2013.2263282
Subject: Support vector machines; Geophysical image processing; Hyperspectral imaging
URI: https://hdl.handle.net/20.500.11815/141

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Citation:

P. Ghamisi, J. A. Benediktsson and M. O. Ulfarsson. (2014). Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields. IEEE Transactions on Geoscience and Remote Sensing, 52(5), 2565-2574. doi: 10.1109/TGRS.2013.2263282

Abstract:

Hyperspectral remote sensing technology allows one to acquire a sequence of possibly hundreds of contiguous spectral images from ultraviolet to infrared. Conventional spectral classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependences, which leads to a dramatic decrease in classification accuracies. In this paper, a new automatic framework for the classification of hyperspectral images is proposed. The new method is based on combining hidden Markov random field segmentation with support vector machine (SVM) classifier. In order to preserve edges in the final classification map, a gradient step is taken into account. Experiments confirm that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies compared to the standard SVM method and also outperforms other studied methods.

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