Metaheuristic pansharpening based on symbiotic organisms search optimization
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CitationYılmaz, C. S., Yılmaz, V., Güngör, O., & Shan, J. (2019). Metaheuristic pansharpening based on symbiotic organisms search optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 167-187.
This study proposed a metaheuristic pansharpening (MP) method, which performs in a Synthetic Variable Ratio (SVR)-like manner. The proposed method introduced the Symbiotic Organisms Search (SOS) algorithm, an advanced nature-based optimization algorithm, to estimate a weight for each multispectral (MS) band to achieve the optimum intensity. The SVR pansharpening formula was used as the objective function and the Root Mean Square Error (RMSE) metric was used as the fitness function of the SOS algorithm to optimize the intensity. The spectral and spatial quality of the results of the MP method were qualitatively and quantitatively compared against those of 15 widely-used pansharpening methods in 5 test sites in Turkey with different land cover features. The experiments aimed to spatially enhance WorldView-2 MS images by using WorldView-2 panchromatic (PAN) bands and a UAV-derived PAN orthophoto. It was also aimed to sharpen IKONOS MS images by using a QuickBird pansharpened image and an IKONOS PAN band. The MATLAB software was used to implement the proposed method and to compute the spatial and spectral quality metrics. The spatial quality of each pansharpened image was evaluated at full-scale, whereas the spectral quality of each pansharpened image was evaluated at both full-scale and reduced-scale. A scoring strategy based on giving a performance score with respect to the spatial and spectral quality metrics was used to ensure a fair comparison among the pansharpening methods used. The results demonstrated that, out of 16 pansharpening methods, the MP method achieved the highest overall spectral and spatial quality scores of 15.5 and 15.5, respectively. The proposed method was found to perform successfully with both singlesensor and multisensor input images. It was also concluded that the proposed method is able to deal with high spatial resolution ratio between the input images.