Remote sensing data fusion using fruit fly optimization

Abstract

The image fusion (pan-sharpening) aims to generate a multispectral image with the maximum of spatial details of the panchromatic image and the spectral characteristics of the multispectral images. The generalized intensity-hue-saturation (GIHS) can produce fused images with high spatial details, but the spectral characteristics of the fused images need improvement. This article introduces an adaptive fusion method based on GIHS using the Fruit Fly Optimization Algorithm (FOA) in two stages. First, we suggest to use it to compute the optimal band weights which reduces the large difference between the intensity component and the panchromatic image. Second, we propose to apply the FOA to compute the modulation parameters that estimate the amount of spatial details to be added to the multispectral images. In this regard, an objective function that combines the coefficient of the correlation (CC) with the spatial coefficient of the correlation (SCC) is suggested. This method is tested on Pleiades, IKONOS, and ALSAT-2A images and we compared it with some existing fusion methods. The CC, the Structural Similarity Index (SSIM), The Root Mean Square Error (RMSE), the Quality with No Reference (QNR), the Relative Global Synthesis Error Metric (ERGAS) and the Relative Average Spectral Error (RASE) are used for quantitative analysis. The best values of CC, RMSE, ERGAS, RASE and QNR on all used datasets are given by the proposed method. The quantitative results and the visual analysis demonstrated that the proposed approach outperforms the four comparison methods in terms of spatial and spectral quality.