成像光谱数据特征选择及小麦品种识别实验研究  

A Experimental Study on Imaging Spectrometer Data Feature Selection and Wheat Type Identification

作　　者：王长耀[1] 刘正军[2] 颜春燕[3] 

机构地区：[1]中国科学院遥感应用研究所遥感信息国家重点实验室,北京100101 [2]中国测绘科学研究院,北京100039 [3]中国地质大学,北京100083

出　　处：《遥感学报》2006年第2期249-255,共7页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家高技术研究发展计划项目(863-308-13-03[02]);国家攻关计划项目(2001DFBA0005);国家重点基础研究发展规划项目(G2000077902);中国科学院知识创新工程重大项目(KZCX1-SW-01)资助

摘　　要：针对河北栾城获得的MAIS成像光谱仪数据用于小麦品种识别进行了特征选择和分类研究。利用遗传算法以JM距离为准则并结合实验区小麦的生物物理特性,进行了最佳波段选择;利用Fuzzy—Artm ap分类器及选出的最佳波段对成像光谱数据进行了分类,区分出了4种小麦品种,小麦的总体分类精度超过97%。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%.

关 键 词：成像光谱仪 波段指数 品种识别 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]