DOI: 10.1016/j.jag.2017.08.015
Scopus记录号: 2-s2.0-85032180686
论文题名: A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images
作者: Keyport R ; N ; , Oommen T ; , Martha T ; R ; , Sajinkumar K ; S ; , Gierke J ; S
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2018
卷: 64 起始页码: 1
结束页码: 11
语种: 英语
英文关键词: Lake atitlán
; Landslide mapping
; Object-Oriented analysis
; Segment optimization
; Unsupervised classification
Scopus关键词: comparative study
; image classification
; image resolution
; landslide
; mapping
; optimization
; orthophoto
; pixel
; remote sensing
; Guatemala [Central America]
; Guatemala [Central America]
; Lake Atitlan
; Solola
英文摘要: A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79882
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geological & Mining Engineering & Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, United States; National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Hyderabad, India; Department of Geology, University of Kerala, Thiruvananthapuram, Kerala, India
Recommended Citation:
Keyport R,N,, Oommen T,et al. A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images[J]. International Journal of Applied Earth Observation and Geoinformation,2018-01-01,64