中文摘要：

近年来,高光谱遥感在林业方面的应用越来越广泛,尤其在分类方面居多。但机载PHI高光谱数据通常用于农业病虫害监测、海洋悬浮物颗粒监测等,在林业方面的应用较少。以湖北省荆门市东宝区为研究区,以机载PHI高光谱遥感数据为数据源,对森林优势树种进行了分类研究。首先采用独立成分分析法（independent component analysis,ICA）对裁剪后的PHI数据进行降噪,并利用自适应波段选择法（adaptive band selection,ABS）进行降维,再采用归一化植被指数（normalized difference vegetation index,NDVI）区分林地与非林地,最后利用支持向量机法（support vector machine,SVM）进行森林优势树种监督分类。研究结果表明,分类精度可达80.70%,Kappa系数达到0.75; 分块处理PHI数据以及采用NDVI区分林地与非林地,对于减弱“同物异谱”和“异物同谱”现象有较好的作用; ABS与SVM相结合的分类方法,较适用于PHI数据在树种识别方面的应用探索,具有重要意义。

英文摘要：

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.