联合GF-5与GF-6卫星数据的多分类器组合亚热带树种识别  

Multiple Classifiers Combination Method for Tree Species Identification Based on GF-5 and GF-6

作　　者：栗旭升 李虎 陈冬花 刘玉锋 刘赛赛 刘聪芳 胡国庆 Li Xusheng;Li Hu;Chen Donghua;Liu Yufeng;Liu Saisai;Liu Congfang;Hu Guoqing(College of Geography and Tourism,Anhui Normal University,Wuhu 241002;National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology,Beijing Research Institute of Uranium Geology,Beijing 100029;College of Computer and Information Engineering,Chuzhou University, Chuzhou 239000;College of Geographical Science and Tourism,Xinjiang Normal University,Urumqi 830054)

机构地区：[1]安徽师范大学地理与旅游学院,芜湖241002 [2]核工业北京地质研究院遥感信息与图像分析技术国家级重点实验室,北京100029 [3]滁州学院计算机与信息工程学院,滁州239000 [4]新疆师范大学地理科学与旅游学院,乌鲁木齐830054

出　　处：《林业科学》2020年第10期93-104,共12页Scientia Silvae Sinicae

基　　金：安徽省高校协同创新项目(GXXT-2019-047);安徽省高校学科优秀拔尖人才学术培育项目(gxbjZD44);滁州市院士助滁项目(20191/S002);芜湖市重点研发项目(2020ms1-3);高分专项省(自治区)域产业化应用项目(76-Y40G05-9001-15/18)。

摘　　要：【目的】针对亚热带森林冠层结构复杂、林分高密度下树种遥感识别精度不高以及不同分类算法对不同树种识别表现力不一等问题,探究高光谱分辨率与高空间分辨率数据联合的多分类器组合树种识别方法,以促进多源数据在森林资源调查和监测领域的深层次应用。【方法】以皇甫山国家森林公园为研究区,联合高分五号AHSI(GF-5 AHSI)与高分六号PMS(GF-6 PMS)卫星数据以及数字高程模型(DEM)、森林资源调查数据等辅助信息,构建亚热带天然次生林复杂冠层结构和高林分密度条件下面向对象多源数据多分类器自适应的树种识别方法。首先利用图割算法(GC)对GF-6 PMS卫星数据进行面向对象多尺度分割,结合外业调查数据选择样本;然后基于GF-5 AHSI卫星数据提取植被指数特征(VIF)26个,GF-6 PMS卫星数据提取各方向各波段纹理特征(TEF)128个,将GF-5 AHSI卫星数据去除坏波段后的304个波段作为光谱特征(SF),基于DEM构建的地形因子作为地形特征(TRF)3个,根据类间可分性和线性判别分析对各类因子进行特征选择,依据特征选择结果构建10种分类方案;最后,采用近邻分类(KNN)、支持向量机(SVM)、贝叶斯分类(Bayesian)、分类回归树(CART)和随机森林(RF)建立基于面向对象的分类精度权重自适应组合分类器(WACC),结合10种分类方案进行树种识别,并对树种识别结果进行精度验证。【结果】线性判别分析模型下,光谱特征、纹理特征和植被指数特征因子分别在增加到28、12和10个后判别精度趋于稳定,对树种具有较好识别能力的光谱特征因子主要集中在红光和近红外波段,纹理特征因子主要集中在均值、熵和角二阶矩,植被指数特征因子主要集中在表征绿度、碳衰减和冠层含水指数;权重自适应分类器组合算法树种识别总体精度为87.51%,Kappa系数为0.854,均优于单一分类器算法;不同特征因子方案在各分�【Objective】In view of the problems that the recognition accuracy of remote sensing tree species under the complex stratospheric structure and high forest density of subtropical forest is not high,and that the performances of different classification algorithms to the recognition of different tree species are various,the method of multi-classor combined tree species recognition of high spectral resolution combined with high spatial resolution data was investigated to promote the in-depth application of multi-source data in the field of forest resource survey and monitoring.【Method】Taken Huangfu Mountain National Forest Park as research area,and assisted with data of GF-6 PMS and GF-5 AHSI,digital elevation model(DEM)and forest resource survey data,the effective tree species recognition algorithm of multi-classor adaptive for object multi-source data,which is under the conditions of subtropical natural secondary forests,complex coronary structures and high forest density,was built in this study.First of all,the GF-6 PMS data was divided according to object-oriented multi-scale and combined with the field survey data by using the graph cutting algorithm(GC)to select the sample.Then,it was carried out to use GF-5 AHSI data to extract 26 vegetation index features(VIF),to use GF-6 PMS data to extract 128 parties of each band texture features(TEF),and combined with 304 bands of GF-5 AHSI with removal of bad band as spectral features(SF)and 3 DEM-built terrain factors as terrain features(TRF,factor features according to various inter-class separability were selected and 10 recognition schemes based on characteristic selection results were built.Finally,the weight adaptive voting combination classifier(WACC)using near-category(KNN),support vector machine(SVM),Bayesian classification(Bayesian),classification regression tree(CART),and random forest(RF)was constructed to recognize tree species based on 10 classification schemes and to verify the accuracy of tree species identification.【Result】Under the linear dis

关 键 词：组合分类器 高分五号 高分六号 树种识别 面向对象 特征选择 

分 类 号：S757[农业科学—森林经理学]