Working band(Central frequence) X (9375 MHz) 
Flight altitude: 
Ground speed: 
Polarization mode: 
Looking mode: 
Swadth width: 
Ground resolution 
5000—10000 M 
450--750 Km/H 
HH,W,HV,VH (changeable in flight) 
Left or Right looking 
(changeable in flight) 
35 Km 
10x10 M 
4. PROCESSING AND ANALYSIS OF HYPERSPECTRAL REMOTE SENSING 
1) . Data processing for the imaging spectrometer 
The data of imaging spectrometer is characterized by the huge volume of information. 
Especially in case of airborne data aquisition, the images aquired are subjected by the 
great distortion. The data are not be suited for the rountine imaging processing. For 
processing and analysis of imaging spectrometer data a series of processing techniques are 
used and developed. 
For image pre-processing, the data format transformation, noise and striping removal, 
radiometric correction and geometric correction are carried out. In case of great distortion 
of image caused by aircraft turbulent a special developed Local Self Adaptive (LSA) algrithm 
is developed for imaging processing. 
2) . Normalization of the image and the Image Cube generation 
For normalization and generation of image cube the following process are carried out: 
Reflectance image transformation. Since the MAIS has not the on-board calibration system. The 
transformation of the imaging spectrometer data from the brightness values into the 
reflectance values pixal by pixal.Two methods can be used for this transformation procedure, 
a. Method of 'Log-residua 1' technique. For this technique the following equation can be used. 
Where Rs is the transformed relative reflectance for each pixal. DN. x is the data number of 
pixals of the imaging spectrometer data in band A, where A =1,2,3 64 (in VIS to SWIR). DN 
s is mean value of all pixals. A, B are the coefficients, which are varing with the mean value 
of each spectral band.For instance of the imaging spectrometer data in bands of 48-56 aquired 
in Xinjiang Area China, 1990. The values of A and B as following: 
Band 48 49 50 51 52 53 54 55 56 
A 947.1 988.5 911.6 1035.2 943.4 901.6 911.7 838.1 771.7 
B -617.5 -624.3 -534.4 -619.4 -504.7 -467.3 -731.5 -628.9 -532.5 
b. Method of 'Training Points' 
For eliminating the atmospheric effect and to make the reflectance image transformation the 
statistical relationship could be set up between the DN values for selected objects of any 
band of images and the reflectance values of corresponding objects Rs measured on the ground 
in same atmospheric and solar conditions. By using the equations established for each band the 
reflectance values for any object can be extropolated. 
C. Spectral feature deriving from the imaging spectrometer image 
From the reflectance image cube the spectral curves can be derived for any single pixal of all 
bands or for a group of pixals corresponding some objects on the ground. 
3). 3-Dimensional Display of the Imaging Spectraometer Data 
The imaging spectrometer data are high dimensional data set. It is very difficult to display 
such data in multidimensional space. Therefore, to reduce the dimensions and to make the 3- D 
display both spacially and spectrally are important for the spectral analysis and applications. 
DN. x 
Rs = Aarc t g 
+ B 
DNs