期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - 4/C7
出版社：Copernicus Publications
摘要：This research has been conducted to evaluate the proposed object-based method with high spatial resolution airborne remote sensing data in tree species identification and mapping. It has been done in 2 areas of total 10 separate areas; include the disturbed natural broadleaved forest and the broadleaved and coniferous mixed forestation in the Northern forests of Iran. After pre-processing the imagery a trial and error method was employed to reach the ideal segmentation results. Subsequent to class definition, sample objects were selected as representative of defined classes and NN classifier was accomplished using integration of a broad spectrum of different object features. Accuracy assessment of the produced maps, comparing with field reference data shows the overall accuracies and Kappa statistics of 0.79, 0.61 (Area1) and 0.76, 0.69 (Area2) respectively. Relatively low accuracy in both areas demonstrated that the standalone optical remote sensing methods are insufficient for classification of such complex forest structures
关键词：Object-based classification; Image segmentation; Ground truth; UltraCamD; High spatial resolution
