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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替代計量分析
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其它資訊
摘要Accurate 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.
著者Zhang HS, Zhang YZ, Lin H
會議名稱IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
會議開始日22.07.2012
會議完結日27.07.2012
會議地點Munich
會議國家/地區德國
詳細描述organized by IEEE,
出版年份2012
月份1
日期1
出版社IEEE
頁次6809 - 6812
電子國際標準書號978-1-4673-1159-5
國際標準期刊號2153-6996
語言英式英語
關鍵詞Classification; Fusion; Random Forest; SAR
Web of Science 學科類別Engineering; Engineering, Electrical & Electronic; Geology; Geosciences, Multidisciplinary; Remote Sensing

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