Remote Sensing Applications and Decision Support

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

[+] Author Affiliations
Reddy Pullanagari, Gábor Kereszturi, Ian J. Yule

Massey University, New Zealand Centre for Precision Agriculture, Soil and Earth Sciences Group, Institute of Agriculture and Environment (IAE), Palmerston North, New Zealand

Pedram Ghamisi

Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Weßling, Germany

Technische Universitat Munchen (TUM), Processing in Earth Observation, Munich, Germany

J. Appl. Remote Sens. 11(2), 026009 (Apr 20, 2017). doi:10.1117/1.JRS.11.026009
History: Received December 5, 2016; Accepted March 28, 2017
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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 with a combination of spectral and spatial information in a single classification framework have attracted 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 a gray 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.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Reddy Pullanagari ; Gábor Kereszturi ; Ian J. Yule and Pedram Ghamisi
"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", J. Appl. Remote Sens. 11(2), 026009 (Apr 20, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.026009


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