Developments in detector technology and microelectronics present new opportunities for remote sensing.For example

the spaceborne Hyperion

flown on EO-1 satellite

acquires image data in 220 spectral bands over the spectral range of 0.4—2.5μm at approximately 0.01μm spectral resolution with 30m spatial resolution.Those hyperspectral data can provide abundant information

and make it possible to detect diagnostical spectral characteristics

such as red-edge drift

etc.However

there is relatively high correlation between different bands and much redundancy in hyperspectral data sets.Therefore

one of the most important procedures is to select optimal bands for extracting information from hyperspectral data effectively.In this paper

we first discuss the characteristics of EO-1/Hyperion instrument

and apply several important pre-processing procedures to Hyperion L1R data

such as radiometric calibration

destriping

smile correction

and geometric correction

etc.Then we apply spectrum reconstruction approach

which uses several basis functions and corresponding spectral intervals to describe the spectrum extracted from Hyperion hyperspectral data sets in Subei region

China.The feature selection method based on spectrum reconstruction is incrementally adding bands to the initial bands

followed by adjustment of band widths and locations.At last

we can aggregate several Hyperion bands into a new simulated band in each interval and apply MLC image classification methods to it.The overall accuracy can be as high as 92% compared with in situ measurement

which supports the validity of this feature selection method.