Abstract
Hyper spectral remote sensing data of many channels, some have hundreds. When display or hardcopy of this kind of data is needed, hyper spectral data users are often confronted with the following question: How to choose 3 channels for RGB combination from so many channels to get the best visual effect?

A method of low to choose the best band combination of hyper spectral data is discussed here. Through mathematical calculation, this method considers high divergence degree and least correlation channels as best band combination. An example study for AVIRIS data (224channels) shows its remark power. And more, because the method is ground feature oriented, it is application for different hyper spectral data users with different application needs.

Introduction
One major advance in remote sensing technology in the past decade is hyper spectral remote sensing. The Jet propulsion Lab (USA) has produced two Hyper spectral sensors, one known as AIS first flown in 1982, the other as AVIRIS (Airborne visible/Infrared Image Spectrometer) which continues to operate since 1987. AVIRIS consists of a total of 224 channels. Europe, China and Japan also have their own experimental hyper spectral imaging system.

The potential uses of hyper spectral data have been studied in various areas, especially in mineral and vegetation distinguishing. Quite a few impressive result have been reported. Some new hyper spectral imaging system will go into operation soon.

When utilizing hyper spectral data, users usually need a special image processing software, like ENVI, which is able to processing and display multi channel information. With the help of this kind of software, researchers could then analyze the spectral characteristic of ground features and get the solution for their application.

In remote sensing image processing, users have often to choose 3 band for display of hardcopy.

For data with fewer bands, like TM, it is not a difficult task. The common RGB combination for TM is 432, 742,453,etc not so easy a job to choose 3 channels for RGB combination to get the best visual effect from hyper spectral data, which have tens of hundreds channels. To solve this problem, we have developed a method in our work. The method takes both channel correlation and divergence degree factors into account.

Method
Obviously, the best band combination varies with application object. Different user have different requirement. Botanists mainly want to distinguish the vegetation, while mineralogists pay much attention to the difference between minerals. They will choose different band combination for display or printing. Hence, when choosing best band combination, user have to9 first determine the ground feature they concern. We call it target ground feature. If users, for example, concern bare land, forest, grass land, snow, and water body, best band combination should have them shown contrast sharpest.