URBAN LAND COVER MAPPING USING RANDOM FOREST COMBINED WITH OPTICAL AND SAR DATA
Refereed conference paper presented and published in conference proceedings


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AbstractAccurate land covers classification is challenging in urban areas due to the diversity of urban land covers. This study presents a classification strategy with combined optical and Synthetic Aperture Radar (SAR) images using Random Forest (RF). Optimization of RF is conducted, indicating the optimal number of decision trees is 10 and the optimal number of features is 4 for splitting each tree node. The overall accuracy (OA) and Kappa coefficient are used to assess the classification. Result shows that classification with combined optical and SAR images (OA: 69.08%; Kappa: 0.6288) is higher than that with single optical image (OA: 81.43%; Kappa: 0.7770). Benefits of the combined use of optical and SAR images mainly come from reducing the confusions between water and shade, and between bare soil and dark impervious surfaces.
All Author(s) ListZhang HS, Zhang YZ, Lin H
Name of ConferenceIEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Start Date of Conference22/07/2012
End Date of Conference27/07/2012
Place of ConferenceMunich
Country/Region of ConferenceGermany
Detailed descriptionorganized by IEEE,
Year2012
Month1
Day1
PublisherIEEE
Pages6809 - 6812
eISBN978-1-4673-1159-5
ISSN2153-6996
LanguagesEnglish-United Kingdom
KeywordsClassification; Fusion; Random Forest; SAR
Web of Science Subject CategoriesEngineering; Engineering, Electrical & Electronic; Geology; Geosciences, Multidisciplinary; Remote Sensing

Last updated on 2020-30-06 at 02:59