【作者】 李凤；

【导师】 周文佐；

【作者基本信息】 西南大学 ， 地图学与地理信息系统， 2022， 硕士

【摘要】 植被类型调查作为地球资源环境监测的重要内容,运用遥感技术进行植被类型大范围识别是一种有效手段,掌握植被类型的分布对地区的资源保护和环境改善具有重要作用。本文对位于秦巴山区过渡带的白水江自然保护区2020年植被采用多分类器集成方法进行类型识别。本研究使用哨兵L2A数据的10个波段以及其余地理辅助特征采用贝叶斯、Cart、KNN、RF和SVM五个单分类器进行植被类型识别,探讨哨兵红边波段以及其余特征对分类的效力;采用Relief F特征选择算法对特征数进行降维;在此基础上采用稳定权重加权投票法(Multiple Classifier Combination Using Weight Vote Algorithm Based Modified Weght,MCC-WVA-MW)和基于DS证据理论方法(KM-DS)两种算法进行多分类器系统的集成,探析不同分类器组合方案中分类效果最优的方案,以期为白水江自然保护区的植被类型研究提供科学依据。主要结论如下:(1)采用5种单分类器对光谱特征分类时,分类效果欠佳,Kappa系数在0.57到0.73之间波动,如果将红边波段剔除,分类精度进一步下降至0.54到0.65之间,虽然仅采用光谱特征分类效果不理想但还是证明了红边波段对分类精度有所提高。将几何形状和纹理特征加入分类时,精度并没有得到提高,而地形特征的加入将Kappa系数提高到0.66-0.83之间,侧面说明并不是特征数越多分类效果越理想。采用Relief F方法对51个特征进行选择最后保留了特征权值前25的特征,其中地形要素和植被指数的权值都排在前位,将这25个特征使用5种单分类器分类时分类效果较理想,有效改善了植被类型被错分的情况,特征选择后OA提高到0.82-0.9之间,Kappa系数提高到0.78-0.88之间,属于效果较好的分类结果。但是各个分类器在植被类型的分布分类上仍然存在着较大差异。(2)采用的MCC-WVA-MW方法对5种分类器的16种组合方案分别进行多分类器系统的集成,其分类结果更加稳定,OA精度差值仅0.04,Kappa系数差值0.05,平均OA和Kappa系数达到0.85和0.82,没有一种组合方案的精度低于0.8,在总体植被类型的分布上,没有出现严重错分情况,是效果理想的分类方法。结合多分类器系统的差异性,在16种方案中表现最佳的一个是Cart、KNN、RF和SVM这四种分类器集成时OA达0.87,Kappa系数是0.85。在这种方法中并没有表现出随着分量分类器个数的增加分类精度增加的趋势,部分3个分类器集成的精度高于5个分类器集成。(3)采用KM-DS方法对多分类器系统进行集成时,分类结果之间还是存在差异的,精度指标的波动比较明显,OA的差值在0.13,Kappa系数的差值在0.15,远高于前一种集成方法,甚至在第6种组合方案(贝叶斯、RF和SVM)中出现严重的水体错分情况。结合多分类器系统的差异性分析,在该方法下最佳的分类器组合方案是KNN、RF和SVM这三种分类器的集成,虽然最佳方案选在三个分量分类器的集成,但是该方法却表现出随着分量分类器数量的增加,平均精度呈现出增加的趋势。更多还原

【Abstract】 The study of vegetation type is an important component in the monitoring of land resources and the environment.Using remote sensing technology to identify large-scale vegetation types is an effective approach.Mastering the distribution of vegetation types plays an important role in the protection of regional resources and the enhancement of the environment.In this paper,the vegetation of the Baishui River Natural Reserve located in the transitional area of Qinba Mountain in 2020 was classified by multiple classifiers integrations.In the study,based on the 10 bands of sentinel L2 A data and other geographical auxiliary features,five simple classifiers of Bayesian,Cart,KNN,RF,SVM were used for vegetation type recognition to explore the effectiveness of sentinel rededge bands and other features on classification.Relief F feature selection algorithm was used to decrease the dimension of feature number.On this basis,the Multiple Classifier Combination Using Weight Vote Algorithm Based Modified Weght(MCC-WVA-MW)and the D-S evidence theory(KM-DS)algorithms were used to integrate multi classifier systems,to explore the schema that had the best classification effect in different classifier combination schemas.It should have provided a scientific basis for the study of vegetation types in the Baishui River Natural Reserve.The main conclusions are as follows:(1)When five single classifiers were used to classify spectral features,the classification effect was not good,and the kappa coefficient ranged from 0.57 to 0.73.If the rededge bands were removed,the accuracy of the classification would be reduced to between 0.54 and 0.65.This showed that the rededge bands increase the accuracy of the classification.When geometric and texture features were added to the classification,the accuracy was not improved.The addition of topographic features increased the kappa coefficient to 0.66-0.83.This showed that not the more the number of features,the better the classification effect.The Relief F algorithm was used to select 51 features,and finally,the top 25 features of feature weight were retained.Among them,the weights of topographic elements and vegetation index ranked in front.When these 25 features were classified by five single classifiers,the classification effect was ideal,which effectively improved the misclassification of vegetation types.After feature selection,OA increased to 0.82-0.9,and kappa coefficient increased to 0.78-0.88.However,there were still great differences in the distribution of vegetation types.(2)The MCC-WVA-MW algorithm integrated single classifiers into a multiple classifier system.The classification results were more stable,the OA accuracy difference was only 0.04,the kappa coefficient difference was 0.05,and the average OA and kappa coefficients were 0.85 and 0.82.There was no combination with an accuracy lower than0.8,which was an ideal classification method.Combined with the diversity measure of multiple classifier systems,the best one among the 16 schemes was the integration of cart,KNN,RF,SVM.The OA of this scheme was 0.87 and the kappa coefficient was 0.85.In this method,there was no trend that the classification accuracy increased with the increase of the number of component classifiers.The accuracy of some three classifiers was higher than that of five classifiers.(3)When using the KM-DS algorithm to integrated multiple classifier systems,there were still differences between classification results,and the fluctuation of the accuracy index was obvious.Its OA difference was 0.13 and kappa coefficient difference was 0.15,which was much higher than the previous integration algorithm.Even in the sixth combination scheme(Bayesian,RF and SVM),there was a serious water misclassification.Combined with the diversity measure of multiple classifier systems,the best classifier combination scheme was the integration of KNN,RF and SVM.Although the best scheme was the integration of three component classifiers,the average accuracy of this method showed an increasing trend with the increase of the number of component classifiers.更多还原