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Proceedings Paper

Evaluating remote sensing image fusion algorithms for use in humanitarian crisis management
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Paper Abstract

This study investigated how different fusion algorithms performed when applied to very high spatial resolution (VHSR) satellite images that encompass ongoing- and post-crisis scenes. The evaluation entailed twelve fusion algorithms. The selected algorithms were applied to GeoEye-1 satellite images taken over three different geographical settings representing natural and anthropogenic crises that had occurred in the recent past: earthquake-damaged sites in Haiti, flood-impacted sites in Pakistan, and armed-conflicted areas and internally displaced persons (IDP) camps in Sri Lanka. Spectral quality metrics included correlation coefficient, peak signal-to-noise ratio index, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence and high-pass correlation coefficient. Under each metric, fusion methods were ranked and best competitors were identified. In this study, the Ehlers fusion, wavelet-PCA fusion (WVPCA), and the high-pass filter fusion algorithms reported the best values for the majority of spectral quality indices. Under spatial metrics, the University of New Brunswick and Gram-Schmidt fusion algorithms reported the optimum values. The color normalization sharpening and subtractive resolution merge algorithms exhibited the highest spectral distortions where as the WV-PCA algorithm showed the weakest spatial improvement. In conclusion, we recommend the University of New Brunswick algorithm if visual image interpretation is involved, whereas the high-pass filter fusion is recommended if semi- or fully-automated feature extraction is involved, for pansharpening VHSR satellite images of ongoing and post crisis sites

Paper Details

Date Published: 25 October 2012
PDF: 12 pages
Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 853807 (25 October 2012); doi: 10.1117/12.973745
Show Author Affiliations
Chandi Witharana, Univ. of Connecticut (United States)
Daniel L. Civco, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 8538:
Earth Resources and Environmental Remote Sensing/GIS Applications III
Shahid Habib; David Messinger; Antonino Maltese; Ulrich Michel; Daniel L. Civco; Manfred Ehlers; Karsten Schulz; Konstantinos G. Nikolakopoulos, Editor(s)

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