An effective approach to selecting the appropriate pan-sharpening method in digital change detection of natural ecosystems

Abstract

In an image fusion process, the spatial resolution of a multispectral image is improved by a panchromatic band. However, due to the spatial and spectral resolution differences between these two data sets, the enhanced image may have two distortions, spatial and spectral. Therefore, to evaluate the efficiency of the pan-sharpening method, the status of these two types of distortions is examined. Unfortunately, there is still no developed acceptance index that can thoroughly investigate the quality of the pansharpened image; moreover, most of the proposed methods for reviewing the quality of output images have been developed with an emphasis on the residential area. Accordingly, to assess the quality of the pansharpened image in this study, we evaluated highly effective conventional methods, such as visual examinations, quantitative evaluation and impact analysis regarding the change detection process of mangrove forests. Finally, we suggested a simple yet efficient approach for such research in natural ecosystems. In the proposed method, based on the nature of the ecosystem, a spectral vegetation index is applied to the pansharpened images; the spectral quality of the images is further evaluated based on two parameters, 1) the areas under the curves of the histogram of the spectral vegetation index in the natural ecosystem region and 2) its centroid. The spatial quality of the pansharpened images is evaluated through implementing of two transects perpendicular to each other in the images of the spectral vegetation index, and creating a spatial deviation on them. With expert reviews and visual evaluation of the pansharpened images, the proposed method, especially in natural ecosystems, has more advantages as regards assessing the quality of the fused images. Based on the evaluations, among 11 methods of pansharpening, including Ehlers Fusion, FuzeGO, Gram-Schmidt, HPF, HCS, PCA, Modified IHS, Brovey Transform, Projective Resolution Merge, Wavelet IHS, and Wavelet PCA; the HPF method the Brovey Transform and Modified IHS methods respectively showed the best performance in the digital change detection of Mangrove forests.