Pullanagari, Reddy and Kereszturi, Gabor and Yule, Ian and Ghamisi, Pedram (2017) Assessing the performance of multiple spectral–spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network. Journal of Applied Remote Sensing, 11 (2), pp. 1-22. Society of Photo-optical Instrumentation Engineers (SPIE). doi: 10.1117/1.JRS.11.026009. ISSN 1931-3195.
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Official URL: http://remotesensing.spiedigitallibrary.org/article.aspx?articleid=2623136
Abstract
Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches witha combination of spectral and spatial information in a single classification framework haveattracted special attention because of their potential to improve the classification accuracy.We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines(SVM) and an artificial neural network. The spatial features considered are produced by agray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.
Item URL in elib: | https://elib.dlr.de/112013/ | |||||||||||||||
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Document Type: | Article | |||||||||||||||
Title: | Assessing the performance of multiple spectral–spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network | |||||||||||||||
Authors: |
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Date: | 2017 | |||||||||||||||
Journal or Publication Title: | Journal of Applied Remote Sensing | |||||||||||||||
Refereed publication: | Yes | |||||||||||||||
Open Access: | Yes | |||||||||||||||
Gold Open Access: | No | |||||||||||||||
In SCOPUS: | Yes | |||||||||||||||
In ISI Web of Science: | Yes | |||||||||||||||
Volume: | 11 | |||||||||||||||
DOI : | 10.1117/1.JRS.11.026009 | |||||||||||||||
Page Range: | pp. 1-22 | |||||||||||||||
Editors: |
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Publisher: | Society of Photo-optical Instrumentation Engineers (SPIE) | |||||||||||||||
ISSN: | 1931-3195 | |||||||||||||||
Status: | Published | |||||||||||||||
Keywords: | hyperspectral; classification; multiple features; gray level co-occurrence matrix;extended multiattribute profiles | |||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | |||||||||||||||
HGF - Program: | Space | |||||||||||||||
HGF - Program Themes: | Earth Observation | |||||||||||||||
DLR - Research area: | Raumfahrt | |||||||||||||||
DLR - Program: | R EO - Earth Observation | |||||||||||||||
DLR - Research theme (Project): | R - Vorhaben hochauflösende Fernerkundungsverfahren (old) | |||||||||||||||
Location: | Oberpfaffenhofen | |||||||||||||||
Institutes and Institutions: | Remote Sensing Technology Institute > SAR Signal Processing | |||||||||||||||
Deposited By: | Ghamisi, Pedram | |||||||||||||||
Deposited On: | 26 Apr 2017 10:37 | |||||||||||||||
Last Modified: | 31 Jul 2019 20:09 |
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