quantitative assessment of transformation based satellite image pan-sharpening algorithms

Background and objectives: pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. due to the large diversities in the utilized pan-sharpening algorithms, occurring spatial and spectral deviations in their results should be recognized by performing the quantitative assessment analysis. methods: in this research, the pan-sharpened images from pca, ihs, and gram-schmidt transformation based algorithms are evaluated for the multi-spectral and panchromatic images fusion of landsat-8 oli sensor (medium scale resolution satellite) and worldview-2 (high-resolution satellite). quantitative analysis is performed on the pan-sharpened products based on the per-pixel deviation (ppd) measure for spectral deviation analysis and high-pass filter and edge extraction measures for analyzing the spatial correlations. moreover, entropy and standard deviation quantitative evaluation measures are also utilized based on the pan-sharpened image content. results: quantitative analysis represents that increasing the spatial resolution of the utilized remote sensing data has direct impacts on the spectral, spatial, and content-based characteristics of the generated pan-sharpened products. gram-schmidt transformation based pan-sharpening method has the least spectral deviations in both worldview-2 and landsat-8 satellite images. but, the amount of spectral, spatial and content-based quantitative measures of pca and ihs are changing with various spatial resolutions. conclusion: it can be said that gram-schmidt pan-sharpening method has the best performance in both medium-scale and high-resolution data sets based on the spectral, spatial, and content quantitative evaluation results. the ihs pan-sharpening method has better performance than the pca method in landsat-8 oli data. but, by increasing the spatial resolution of the data, pca generates pan-sharpened products with better spectral, spatial, and content based quantitative evaluation results.