林业科学2017，Vol.53Issue(12)：73-83,11.DOI:10.11707/j.1001-7488.20171208
分割尺度对面向对象树种分类的影响及评价
Effect and Evaluation of Segmentation Scale on Object-Based Forest Species Classification
摘要
Abstract
[Objective] The effects of different segmentation scales on the object-oriented tree species classification based on high spatial resolution remote sensing image and spaceborne polarimetric SAR data collaborated were studied,and the suitability of tree species classification based on the two kinds of data collaborated was also evaluated in this research.[Method] QuickBird remote sensing image and Radarsat-2 data are used as the experimental data.3 segmentation schemes,using QuickBird remote sensing image only,using Radarsat-2 data only,and using QuickBird and Radarsat-2 together,are applied in the object-oriented classification.In every segmentation scheme,10 segmentation scales (25-250,step 25) are adopted,and the modified Euclidean distance 3 (ED3modified) is used to to evaluate the segmentation quality.In the 3 segmentation schemes,the respective characteristics and the common characteristics are applied separately in support vector machine classifier to carry on object-oriented tree species classification.[Result] On the 10 segmentation scales,the values of ED3modified of segmentation with QuickBird and Radarsat-2 collaborated and QuickBird only are significantly lower than those with Radarsat-2 only.The best segmentation (ED3modified =O.34) is gotten at scale 100 with QuickBird and Radarsat-2 collaborated.On the 10 segmentation scales,the OA of 3 segmentationclassification schemes are low at the small scales.The OA improves as the scale becomes bigger,and reaches the maximum at a scale.Then the OA reduces with the scale increasing.The segmentation-classification using QuickBird and Radarsat-2 together gets the best accuracy at scale 100 (OA =85.55％;Kappa =0.86),and the scheme using QuickBird remote sensing image alone gets the best accuracy at 150 (OA =81.11％;Kappa =0.82),the scheme using Radarsat-2data alone gets the best accuracy at 125 (OA =66.67％;Kappa =0.68),OA and ED3modifled are highly correlated (R2 =0.73).[Conclusion] At all scales(25-250),the segmentation quality and accuracy of using QuickBird and Radardat-2 together are better than any other segmentation result and accuracies of using only one source of data,and has obvious advantages compared to only use Radarsat-2 data.Segmentation scale plays an important role in tree species classification.Good matching segmentation and reference objects can get higher classification accuracies.At the same time,the classification results are not obviously influenced by slightly over segmentation or insufficient segmentation.关键词
影像分割/尺度参数/SAR/QuickBird/Radarsat/支持向量机Key words
image segmentation/scale parameter/SAR/QuickBird/Radarsat/SVM分类
农业科技引用本文复制引用
毛学刚,陈文曲,魏晶昱,范文义..分割尺度对面向对象树种分类的影响及评价[J].林业科学,2017,53(12):73-83,11.基金项目
国家重点研发计划(2017YFD0600902) （2017YFD0600902）
国家自然科学基金项目(31300533). （31300533）
