Research Papers

Assessment of image fusion procedures using entropy, image quality, and multispectral classification

[+] Author Affiliations
J. Wesley Roberts

Natural Resources and Environment, Ecosystems, Forestry and Forest Products Research Centre, Council for Scientific and Industrial Research, P.O. Box 17001, Durban, Kwazulu-Natal 4013 South Africa

Jan Van Aardt

Natural Resources and Environment, Ecosystems, Earth Observations, Council for Scientific and Industrial Research, P.O. Box 395, Pretoria, 0001 South Africa

Fethi Ahmed

School of Environmental Sciences, University of Kwazulu-Natal, King George V Avenue, Durban, Kwazulu-Natal 4041 South Africa

J. Appl. Remote Sens. 2(1), 023522 (May 29, 2008). doi:10.1117/1.2945910
History: Received November 27, 2007; Revised April 25, 2008; Accepted May 20, 2008; May 29, 2008; Online May 29, 2008
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Abstract

The use of disparate data sources within a pixel level image fusion procedure has been well documented for pan-sharpening studies. The present paper explores various image fusion procedures for the fusion of multi-spectral ASTER data and a RadarSAT-1 SAR scene. The research sought to determine which fusion procedure merged the largest amount of SAR texture into the ASTER scenes, while also preserving the spectral content. An additional application based maximum likelihood classification assessment was also undertaken. Three SAR scenes were tested namely, one backscatter scene and two textural measures calculated using grey level co-occurrence matrices (GLCM). Each of these were fused to the ASTER data using the following established approaches; Brovey transformation, Intensity Hue and Saturation, Principal Component Substitution, Discrete wavelet transformation, and a modified discrete wavelet transformation using the IHS approach. Resulting data sets were assessed using qualitative and quantitative (entropy, universal image quality index, maximum likelihood classification) approaches. Results from the study indicated that while all post fusion data sets contained more information (entropy analysis), only the frequency-based fusion approaches managed to preserve the spectral quality of the original imagery. Furthermore results also indicated that the textural (mean, contrast) SAR scenes did not add any significant amount of information to the post-fusion imagery. Classification accuracy was not improved when comparing ASTER optical data and pseudo optical bands generated from the fusion analysis. Accuracies range from 68.4% for the ASTER data to well below 50% for the component substitution methods. Frequency based approaches also returned lower accuracies when compared to the unfused optical data. The present study essentially replicated (pan-sharpening) studies using the high resolution SAR scene as a pseudo panchromatic band.

© 2008 Society of Photo-Optical Instrumentation Engineers

Citation

J. Wesley Roberts ; Jan Van Aardt and Fethi Ahmed
"Assessment of image fusion procedures using entropy, image quality, and multispectral classification", J. Appl. Remote Sens. 2(1), 023522 (May 29, 2008). ; http://dx.doi.org/10.1117/1.2945910


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