DOI: 10.1016/j.jag.2016.01.011
Scopus记录号: 2-s2.0-85017406334
论文题名: A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments
作者: Li M ; , Ma L ; , Blaschke T ; , Cheng L ; , Tiede D
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 49 起始页码: 87
结束页码: 98
语种: 英语
英文关键词: Classification
; Feature selection
; GEOBIA
; High spatial resolution
; Mixed object
; OBIA
; Random Forest
; Segmentation scale
; Training set size
Scopus关键词: image analysis
; image classification
; imagery
; remote sensing
; segmentation
; spatial resolution
; support vector machine
; China
英文摘要: Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80078
Appears in Collections: 气候变化事实与影响
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作者单位: Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, China; Department of Geoinformatics – Z_GIS, University of Salzburg, Hellbrunner Str. 34Salzburg, Austria
Recommended Citation:
Li M,, Ma L,, Blaschke T,et al. A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,49