DocumentCode :
3072261
Title :
Who does what where? Advanced earth observation for humanitarian crisis management
Author :
Witharana, C.
Author_Institution :
Center for Integrative Geosci., Univ. of Connecticut, Storrs, CT, USA
fYear :
2012
fDate :
27-29 Sept. 2012
Firstpage :
1
Lastpage :
6
Abstract :
This study investigated the performances of data fusion algorithms when applied to very high spatial resolution satellite images that encompass ongoing- and post-crisis scenes. The evaluation entailed twelve fusion algorithms. The candidate 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 in Sri Lanka. Fused images were assessed subjectively and objectively. 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 principle component analysis (WV-PCA) fusion, 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, this study recommends 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 on-going and post crisis sites.
Keywords :
disasters; edge detection; emergency management; feature extraction; geophysical image processing; image colour analysis; image fusion; image representation; image resolution; merging; natural scenes; principal component analysis; spectral analysis; wavelet transforms; Canny edge correspondence; Ehlers fusion; GeoEye-1 satellite images; Gram-Schmidt fusion algorithm; Haiti; Pakistan; Sri Lanka; University of New Brunswick algorithm; VHSR satellite image pansharpening; WV-PCA fusion; advanced Earth observation data; anthropogenic crisis representation; armed-conflicted areas; color normalization sharpening algorithm; data fusion algorithm performance investigation; earthquake-damaged sites; flood-impacted sites; fully-automated feature extraction; high-pass correlation coefficient; high-pass filter fusion algorithm; humanitarian crisis management; image fusion; mean structural similarity index; natural crisis representation; ongoing-crisis scenes; peak signal-to-noise ratio index; post-crisis scenes; relative dimensionless global error; semi-automated feature extraction; spatial integrity; spectral angle mapper; spectral distortion; spectral quality metrics; subtractive resolution merge algorithm; very high spatial resolution satellite images; visual image interpretation; wavelet principal component analysis fusion; Algorithm design and analysis; Measurement; PSNR; Satellites; Spatial resolution; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1976-8
Type :
conf
DOI :
10.1109/ICIAFS.2012.6420035
Filename :
6420035
Link To Document :
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