Remote sensing data fusion using fruit fly optimization
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Remote sensing data fusion using fruit fly optimization Abdelwhab Ouahab 1,2
& Mohamed Faouzi Belbachir
1
Received: 5 August 2019 / Revised: 11 August 2020 / Accepted: 2 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Fruit fly . Pan-sharpening . Image fusion . KONOS . Optimization . FOA
* Abdelwhab Ouahab ouahab.abdelwhab@univ–usto.dz
1
Laboratoire Signaux, Systèmes et Données (LSSD), Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouer, 31000 Oran, Algeria
2
Department of Mathematics and Computer Science, African University Ahmed Draia, Adrar, Algeria
Multimedia Tools and Applications
1 Introduction Many satellite sensors, such as Landsat8, SPOT, QuickBird, ALSAT-2A, and IKONOS produce two types of images. The first one is a Multispectral Image (MS) with a low spatial resolution. The second type is a Panchromatic image (PAN) with a high spatial resolution [21]. The aim of pan-sharpening is to merge the high spatial resolution of the PAN image with the spectral information of the MS images in order to obtain high-resolution MS images [38]. This kind of image fusion is beneficial for various remote sensing applications such as classification, detection, segmentation, environment monitoring, agricultural management, mapping, and feature extraction [7, 38]. A considerable number of strategies and methods for image fusi
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