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

Title
Improving image classification in a complex wetland ecosystem through image fusion techniques
Publication Date
2014
Author(s)
Kumar, Lalit
( author )
OrcID: https://orcid.org/0000-0002-9205-756X
Email: lkumar@une.edu.au
UNE Id une-id:lkumar
Sinha, Priyakant
Taylor, Subhashni
( author )
OrcID: https://orcid.org/0000-0002-1624-0901
Email: btaylo26@une.edu.au
UNE Id une-id:btaylo26
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
International Society for Optical Engineering (SPIE)
Place of publication
United States of America
DOI
10.1117/1.JRS.8.083616
UNE publication id
une:15877
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.
Link
Citation
Journal of Applied Remote Sensing, 8(1), p. 083616-1-083616-16
ISSN
1931-3195
Start page
083616-1
End page
083616-16

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