Multispectral image visualization through first-order fusion

We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first-order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first-order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed. A variety of experimental results are presented to support the performance of the new method.

I. Introduction

The ADVENT of new remote sensing and imaging technologies provides us with ever increasing volumes of multispectral data. Faced with this information explosion, it has become necessary to develop methods for analysis of such high dimensional datasets. One key aspect of this process is the visualization of multispectral data, to be used for photointerpretation. This allows an image analyst to determine regions of interest and important features in the image for further analysis or segmentation. In order to take full advantage of the human visual system, a Red-Green-Blue composite image is usually generated from the data by one of a number of statistical methods which we review in Section II. We aim to produce a one-band, grayscale visualization image from a given multispectral dataset. We propose to do this in such a way as to preserve as much local image contrast “feature information” as possible.