文章基本信息

标题：Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery
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作者：H. Tonbul ; I. Colkesen ; T. Kavzoglu 等
期刊名称：Journal of Geodetic Science
印刷版ISSN：2081-9919
电子版ISSN：2081-9943
出版年度：2020
卷号：10
期号：1
页码：14-22
DOI：10.1515/jogs-2020-0003
出版社：Walter de Gruyter GmbH
摘要：The poplar species in the forest ecosystems are one of the most valuable and beneficial species for the society and environment. Conventional methods require high cost, time and labor need, and the results obtained vary and are insu˚cient in terms of achieved accuracy level. Determination of poplar cultivated fields and mapping of their spatial sites play a vital role for decision-makers and planners to enhance the economic and ecological value of poplar trees. The study aims to map Poplar (P. deltoides ) cultivated areas in Akyazi district of Sakarya, Turkey province using various combinations of the Sentinel-2A image bands. For this purpose, object-based classification based on multi-resolution segmentation algorithm was utilized to produce image objects and ensemble learning algorithms, namely, Adaboost (AdaB), Random Forest (RF), Rotation Forest (RotFor) and Canonical correlation forest (CCF) were applied to produce thematic maps. In order to analyze the effects of the spectral bands of the Sentinel-2A image on the object-based classification performance, three datasets consisting of different spectral band combinations (i.e. four 10 m bands, six 20 m bands and ten 10m pan-sharpened bands) were used. The results showed that the RotFor and CCF classifiers produced superior classification performances compared to the AdaB and RF classifiers for the band combinations regarded in this study. Moreover, it was found that determination of poplar tree class level accuracy reached to ~94% in terms of F-score. It was also observed that the inclusion of the six spectral bands at 20 m resolution resulted in a noteworthy increase in classification accuracy (up to 6%) compared to single 10m band combination.
关键词：Ensemble Learning ; Poplar Trees ; Object-based Image Analysis ; Sentinel-2A