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

被引：26
|
作者：
Roberts, J. Wesley [1 ]
Van Aardt, Jan [2 ]
Ahmed, Fethi [3 ]
机构：
[1] Council for Scientific and Industrial Research, Natural Resources and the Environment, Ecosystems, Forestry and Forest Products Research Centre, P.O.Box 17001, Congella, 4013, South Africa
[2] Cncl. for Sci. and Indust. Res., Nat. Resources and the Environment, Ecosystems, Earth Observation, P.O. Box 395, Pretoria, 0001, South Africa
[3] University of Kwazulu-Natal, School of Environmental Sciences, King George V Avenue, Glenwood, Durban, 4041, South Africa
来源：
Journal of Applied Remote Sensing | 2008年 / 2卷 / 01期
关键词：
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;
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