IMAGE FUSION METHODS

In previous post we saw introduction of Image Fusion and Evolution of Image Fusion Research. So let's move towards the main topic of discussion that is the Methods using which Image Fusion can be done.

IMAGE FUSION METHODS

          There are various image fusion methods available, but the point is that every new method is based on the common characteristics on basics method.

The good image fusion method has the following properties. 

First, it can preserve most of the useful information of different images. 

Second, it does not produce artifacts which can distract or mislead a human observer or any subsequent image processing steps. 

Third it must be reliable and robust. Finally it should not discard any salient information contained in any of the input images.

These are some basic image fusion methods.

• Intensity-Hue-Saturation (IHS) Image Fusion Method

IHS method is a common way of fusing high spatial resolution, single band, pan and low spatial resolution, multispectral remote sensing image. The R, G and B bands of the multispectral image are transformed into HIS components, replacing the intensity component by the pan image, and performing the inverse transformation to obtain a high spatial resolution multispectral image.
HIS can enhance spatial details of the multispectral image and improve the textural characteristics of the fused, but the fusion image exist serious spectral distortion.

• The Brovey Transform image fusion

The BT method is based on the chromaticity transform. It is a simple method for combining data from multiple sensors with the limitation that only three bands are involved. It purpose is to normalize the three multispectral bands used for RGB display and to multiply the result by any other desired data to add the intensity or brightness component to the image.

This technique requires an experienced analyst for the specific adaptation of parameters. This produces development of a user friendly automated tool. The Brovey Transform was developed to avoid the disadvantages of the IHS method.

• Principal Component Analysis 

PCA transformation is a technique from statistics for simplifying a data set. The aim of the method is to reduce the dimensionality of multivariate data whilst preserving as much of the relevant information as possible.

It translates correlated data set to uncorrelated dataset. PCA data are often more interpretable than the source data. By using this method, the redundancy of the image data can be decreased.

The PCA involves a mathematical procedure that transforms a number of correlated variables into a number of uncorrelated variables called principal components. It computes a compact and optimal description of the data set.