Distance-based separability criterion of ROI in classification of farmland hyper-spectral images  

作　　者：Tang Jinglei Miao Ronghui Zhang Zhiyong Xin Jing Wang Dong 

机构地区：[1]College of Information Engineering,Northwest A&F University,Yangling 712100,Shaanxi,China [2]Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xi’an University of Technology,Xi’an 710048,China [3]College of Information Science and Engineering,Shanxi Agricultural University,Taigu 030801,Shanxi,China

出　　处：《International Journal of Agricultural and Biological Engineering》2017年第5期177-185,共9页国际农业与生物工程学报（英文）

基　　金：supported by the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing under Grant No.2016CP01,Xi’an University of Technology,Xi’an Science and Technology Plan Projects under Grant No.NC1504(2);the National Natural Science Foundation of China under Grant No.31101075;the National High Technology Research and Development of China(863 Program)under Grant No.2013AA10230402,Natural Science Fundamental Research Plan of Shaanxi Province under Grant No.2016JM6038;Fundamental Research Funds for the Central Universities,NWSUAF,China,Grant No.2452015060.

摘　　要：The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects classification.Despite the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor environment.This research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral images.It involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition firstly.Then maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects classification.The performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.

关 键 词：distance-based separability criterion near-infrared hyper-spectral image ROI farmland image classification 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]