【摘要】 以太行山区经济林种植区为研究对象，通过无人机高光谱遥感数据，构建不同经济林树种高光谱特征数据库，利用CART决策树、最大似然法(Maximum likelihood classifier, MLC)、随机森林(Random forest, RF)和支持向量机(Support vector machine, SVM)等方法，获得高光谱遥感经济林树种最优识别模型。研究结果表明：(1)苹果、杏、柿、樱桃、核桃的反射峰在550 nm、750～950 nm及960 nm附近的水汽吸收带差异明显；(2)简单比值指数(SR)、类胡萝卜素反射指数2(CRI2)、绿波段指数(GRVI)等7种植被指数重要性评分大于0.05,利于经济林树种识别；(3)基于光谱特征波段、植被指数、纹理特征的组合方式通过SVM的分类效果最好，优于MLC和RF算法，总体精度(Overall accuracy, OA)达到95.11%,Kappa系数为0.915 8。综上所述，基于特征波段、植被指数、纹理特征3种特征组合并采用支持向量机(SVM)分类的识别方法，为6种树种识别的最佳识别方法。更多还原

【Abstract】 The economic forest planting area in Taihang Mountain Area was taken as the research object. Based on the hyperspectral remote sensing data of unmanned aerial vehicle, the hyperspectral characteristic database of different economic forest species was constructed, and the optimal identification model of economic forest species by hyperspectral remote sensing was obtained by using CART decision tree, maximum likelihood classifier(MLC), random forest(RF) and support vector machine(SVM). The results showed that:(1) The water vapor absorption bands of apple, apricot, persimmon, cherry and walnut were obviously different around the reflection peak of 550 nm, between 750-950 nm and around 960 nm;(2) The simple ratio index(SR), carotenoid reflex index 2(CRI2), green band index(GRVI) and other 7 plants were more than 0.05, which were beneficial to the identification of economic forest species;(3) SVM was the best classification method based on spectral characteristic band, vegetation index and texture feature, which was better than MLC and RF algorithm. The overall accuracy(OA) was 95.11% and Kappa coefficient was 0.915 8. To sum up, based on the combination of characteristic band, vegetation index and texture features, the identification method of support vector machine(SVM) classification was the best identification method for six tree species.更多还原