A Experimental Study on Imaging Spectrometer Data Feature Selection and Wheat Type Identification
成像光谱数据特征选择及小麦品种识别实验研究

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%.