国土资源遥感2017，Vol.29Issue(2)：110-116,7.DOI:10.6046/gtzyyg.2017.02.16
基于机载PHI高光谱数据的森林优势树种分类研究
Classification of forest species using airborne PHI hyperspectral data
摘要
Abstract
Hyperspectral data are becoming more and more widely used in forestry, especially in terms of classification.Nevertheless, the application of PHI in forestry is much less than that in such fields as agricultural pest and disease monitoring and marine suspended particles monitoring.PHI is used in this paper, and the study area is Jingmen in Hubei Province.This paper proposes an independent component analysis (ICA) combined with adaptive band selection (ABS) algorithm to reduce dimensions, extract forest land and non-forest land using (normalized difference vegetation index,NDVI) based on the subset images, and finally classify the images by support vector machine (SVM), with the overall classification accuracy being 80.70%, and Kappa coefficient reaching 0.75.The results show that the chunk of PHI data and the use of the extraction of NDVI to distinguish between forest land and non-forest land to decrease the effect of "the same object with different spectra" and "the same spectrum with different objects" can yield a good effect.It is shown that the combination of ICA-ABS and SVM is suitable for PHI data.This study has an important significance for the application of hyperspectral in tree species recognition.关键词
高光谱数据/PHI/降维/波段选择法/SVMKey words
hyperspectral data/PHI/dimensionality reduction/band selection method/SVM分类
信息技术与安全科学引用本文复制引用
樊雪,刘清旺,谭炳香..基于机载PHI高光谱数据的森林优势树种分类研究[J].国土资源遥感,2017,29(2):110-116,7.基金项目
高分辨率对地观测系统重大专项项目(编号:30-Y20A37-9003-15/17)、国家自然科学基金青年科学基金项目"机载激光雷达探测森林冠层高度的机理模型研究"(编号:41201334)和国家高技术研究发展计划(863计划)子课题"全球林业定量遥感专题产品生产体系(二)"(编号:2013AA12A302)共同资助. （编号:30-Y20A37-9003-15/17）
