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Journal of Applied Remote Sensing • Open Access

Improving image classification in a complex wetland ecosystem through image fusion techniques
Author(s): Lalit Kumar; Priyakant Sinha; Subhashni Taylor

Paper 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.

Paper Details

Date Published: 11 June 2014
PDF: 16 pages
J. Appl. Remote Sens. 8(1) 083616 doi: 10.1117/1.JRS.8.083616
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Lalit Kumar, Univ. of New England (Australia)
Priyakant Sinha, Univ. of New England (Australia)
Subhashni Taylor, Univ. of New England (Australia)


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