Improving image classification in a complex wetland ecosystem through image fusion techniques

Kumar, Lalit, Sinha, Priyakant, and Taylor, Subhashni (2014) Improving image classification in a complex wetland ecosystem through image fusion techniques. Journal of Applied Remote Sensing, 8. 083616.

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Abstract

The aim of this study was to evaluate the impact of image fusion techniques on vegetation classification accuracies in a complex wetland system. Fusion of panchromatic (PAN) and multispectral (MS) Quickbird satellite imagery was undertaken using four image fusion techniques: Brovey, hue-saturation-value (HSV), principal components (PC), and Gram– Schmidt (GS) spectral sharpening. These four fusion techniques were compared in terms of their mapping accuracy to a normal MS image using maximum-likelihood classification(MLC) and support vector machine (SVM) methods. Gram–Schmidt fusion technique yielded the highest overall accuracy and kappa value with both MLC (67.5% and 0.63, respectively) and SVM methods (73.3% and 0.68, respectively). This compared favorably with the accuracies achieved using the MS image. Overall, improvements of 4.1%, 3.6%, 5.8%, 5.4%, and 7.2% in overall accuracies were obtained in case of SVM over MLC for Brovey, HSV, GS, PC, and MS images, respectively. Visual and statistical analyses of the fused images showed that the Gram–Schmidt spectral sharpening technique preserved spectral quality much better than the principal component, Brovey, and HSV fused images. Other factors, such as the growth stage of species and the presence of extensive background water in many parts of the study area, had an impact on classification accuracies.

Item ID: 63696
Item Type: Article (Research - C1)
ISSN: 1931-3195
Keywords: classification; image fusion; saltmarsh vegetation; multispectral; panchromatic
Copyright Information: © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Date Deposited: 02 Sep 2020 01:57
FoR Codes: 09 ENGINEERING > 0909 Geomatic Engineering > 090903 Geospatial Information Systems @ 30%
09 ENGINEERING > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing @ 30%
05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050205 Environmental Management @ 40%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960503 Ecosystem Assessment and Management of Coastal and Estuarine Environments @ 100%
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