With the development of imaging spectrometer technology

the ground objects’ consecutive information from it makes it possible to identify different vegetation types

though some relevant research was carried out in the past few years

most are about forestry

yet few about crops.Further

there exist strong correlation between bands of imaging spectrometer

so how to reduce as much as possible the redundant information and reserve useful information appear much more important.This paper first did feature selection based on genetic algorithm（GA） and wheat biophysical characteristics.In feature selection using GA

for the training samples

when combined bands reach 4

the JM distance of optimal combination reach much high level

when bands go on increasing

the average JM distance increases slowly until when bands reach 8

the distance does not increase further

so the optimal bands combination can be obtained.In feature selection using wheat biophysical characteristics

we found that there appear strong correlative bands for wheat protein and dry gluten with spectra

so the sensitive bands can be obtained.Combining these two feature selection steps

the ultimate optimal bands combination was given.After feature selection

we use the selected bands and classifier Fuzzy-Artmap to classify the imaging spectrometer data.It showed that for 4 wheat types

they can be identified clearly

the average classification accuracy is above 90%.