Digital Image Fusion Techniques: A Review

Abstract

Image Fusion is a technique of obtaining images with high spatial and spectral resolution from low spatial resolution multispectral and high spatial resolution panchromatic images. There is often an inverse relationship between the spectral and spatial resolution of the image. It has not been possible to propose a single sensor package that will meet all our application requirements, while the combined image from multiple sensors will provide more comprehensive information by collecting a wide diversity of sensed wavelengths and spatial resolutions. Due to the demand for higher classification accuracy and the need in enhanced positioning precision there is always a need to improve the spectral and spatial resolution of remotely sensed imagery. These requirements can be fulfilled by the utilization of image processing techniques at a significantly lower expense. The goal is to combine image data to form a new image that contains more interpretable information than can be gained by using the original information. Ideally the fused data should not distort the spectral characteristics of multispectral data as well as it should retain the basic colour content of the original data. If the fused images are used for classification, then the commonly used merging methods are Principal Component Analysis(PCA), Intensity hue saturation method (IHS), Brovey transformation, multiplicative technique (MT), High-pass Filter (HPF), Smoothing Filter-based Intensity Modulation(SFIM) and Wavelet Transform.