Analysis of the Spatial and Spectral Distortions by Using Fusion Methods

the purposes of human/machine perception, and for further Abstract: Image processing techniques primarily focus image-processing tasks such as segmentation, object upon enhancing the quality of an image or a set of detection or target recognition in applications such as images and to derive the maximum information from remote sensing and medical imaging. For example, visiblethem. Image Fusion is such a technique of producing a band and infrared images may be fused to aid pilots superior quality image from a set of available images. It landing aircraft in poor visibility. is the process of combining relevant information from two or more images into a single image wherein the Multi-sensor images often have different geometric resulting image will be more informative and complete representations, which have to be transformed to a than any of the input images. Fusion Methods combines common representation for fusion. This representation a low-resolution color multispectral image with a highshould retain the best resolution of either sensor. A resolution grayscale panchromatic image to create a prerequisite for successful in image fusion is the alignment high-resolution fused color image. In this paper we of multi-sensor images. Multi-sensor registration is also examine five different Fusion Methods: Brovey affected by the differences in the sensor images. Transform, IHS, PCA, Wavelet fusion, “Mallat” algorithm, and VWP and evaluate their effectiveness. Multi-sensor images often have different Additionally, we propose an extension to the IHS geometric representations, which have to be transformed to Fusion Methods method to improve the resulting a common representation for fusion. This representation spectral quality. In order to compare the method should retain the best resolution of either sensor. A results we evaluate spatial and spectral qualities by prerequisite for successful in image fusion is the alignment relying on both visual inspection and metric of multi-sensor images. Multi-sensor registration is also performance data. Our results indicate that VWP is affected by the differences in the sensor images. most effective in preserving spectral data, while IHS methods produce images with the best spatial quality. Image fusion is the process by which two or more images are combined into a single image retaining the IndexTerms—Fusion, pan sharpening, quality important features from each of the original images. The assessment, very high resolution satellites. fusion of images is often required for images acquired from different instrument modalities or capture techniques of the same scene or objects (like multi-sensor, multi-focus