基于多源遥感数据的面向对象林分类型识别

Object-oriented stand type classification based on the combination of multi-source remotesen-sing data

林分类型的识别是森林资源监测的核心问题之一.为研究多源遥感数据协同的面向对象林分类型分类识别,采用Radarsat-2数据和Quick Bird遥感影像协同进行面向对象分类.在面向对象分类过程中,采用3种分割方案:单独使用Quick Bird遥感影像分割;单独使用Radarsat-2数据分割;Radarsat-2&Quick Bird协同分割.3种分割方案均采用10种分割尺度(25~250,步长25),应用修正的欧式距离3指标评价不同分割方案的分割结果,确定最优分割方案及最优分割尺度.在最优分割结果的基础上,基于地形、高度、光谱及共同特征的不同特征组合,应用带有径向基(RBF)核函数的支持向量机(SVM)分类器进行杉木林、马尾松林、阔叶林3种林分类型识别.结果表明:与单独使用一种数据相比,Radarsat-2数据和Quick Bird遥感影像协同方案在面向对象林分类型分类方面具有优势.Radarsat-2&Quick Bird协同分割方案,以最优尺度参数100进行分割时,分割结果最好.在最优分割结果的基础上,应用两种数据源提取的全部特征进行面向对象林分类型识别的精度最高(总精度为86%,Kappa值为0.86).本研究结果不仅可为多源遥感数据结合进行林分类型识别提供参考和借鉴,而且对于森林资源调查和监测有现实意义.

The recognition of forest type is one of the key problems in forest resource monitoring.The Radarsat-2 data and Quick Bird remote sensing image were used for object-based classification to study the object-based forest type classification and recognition based on the combination of multisource remote sensing data. In the process of object-based classification,three segmentation schemes（ segmentation with Quick Bird remote sensing image only,segmentation with Radarsat-2 data only,segmentation with combination of Quick Bird and Radarsat-2） were adopted. For the three segmentation schemes,ten segmentation scale parameters were adopted（ 25-250,step 25）,and modified Euclidean distance 3 index was further used to evaluate the segmented results to determine the optimal segmentation scheme and segmentation scale. Based on the optimal segmented result,three forest types of Chinese fir,Masson pine and broad-leaved forest were classified and recognized using Support Vector Machine（ SVM） classifier with Radial Basis Foundation（ RBF） kernel according to different feature combinations of topography,height,spectrum and common features. The results showed that the combination of Radarsat-2 data and Quick Bird remote sensing image had its advantages of object-based forest type classification over using Radarsat-2 data or Quick Bird remote sensing image only. The optimal scale parameter for Quick BirdRadarsat-2 segmentation was 100,and at the optimal scale,the accuracy of object-based forest type classification was the highest（ OA =86%,Kappa = 0. 86）,when using all features which were extracted from two kinds of data resources. This study could not only provide a reference for forest type recognition using multi-source remote sensing data,but also had a practical significance for forest resource investigation and monitoring.