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
Pan-sharpening of moderate resolution multispectral remote sensing data with those of a higher spatial resolution is a standard practice in remote sensing image processing. This paper suggests a method by which the spatial properties of resolution merge products can be assessed. Whereas there are several accepted metrics, such as correlation and root mean square error, for quantifying the spectral integrity of fused images, relative to the original multispectral data, there is less agreement on a means by which to assess the spatial properties, relative to the original higher-resolution, pansharpening data. In addition to qualitative, visual, and somewhat subjective evaluation, quantitative measures used have included correlations between high-pass filtered panchromatic and fused images, gradient analysis, wavelet analysis, among others. None of these methods, however, fully exploits the spatial and structural information contained in the original high resolution and fused images. This paper proposes the use of the Fourier transform as a means to quantify the degree to which a fused image preserves the spatial properties of the pan-sharpening high resolution data. A highresolution 8-bit panchromatic image was altered to produce a set of nine different test images, as well as a random image. The Fourier Magnitude (FM) image was calculated for each of the datasets and compared via FM to FM image correlation. Furthermore, the following edge detection algorithms were applied to the original and altered images: (a) Canny; (b) Sobel; and (c) Laplacian. These edge-filtered images were compared, again by way of correlation, with the original edge-filtered panchromatic image. Results indicate that the proposed method of using FTMI as a means of assessing the spatial fidelity of high-resolution imagery used in the data fusion process outperforms the correlations produced by way of comparing edge-enhanced images.
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