Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines

Nguyen Thanh Son, Chi Farn Chen, Cheng Ru Chen, Vo Quang Minh

研究成果: 雜誌貢獻期刊論文同行評審

51 引文 斯高帕斯(Scopus)

摘要

This study developed an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A (S1A) data. The data were processed through four steps: (1) data pre-processing, (2) constructing smooth time series VH backscatter data, (3) rice crop classification using random forests (RF) and support vector machines (SVM) and (4) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of rice-cropping patterns in the study region. The comparisons between the classification results and the ground reference data indicated that the overall accuracy and Kappa coefficient achieved from RF were 86.1% and 0.72, respectively, which were slightly more accurate than SVM (overall accuracy of 83.4% and Kappa coefficient of 0.67). These results were reaffirmed by the government’s rice area statistics with the relative error in area (REA) values of 0.2 and 2.2% for RF and SVM, respectively.

原文???core.languages.en_GB???
頁(從 - 到)587-601
頁數15
期刊Geocarto International
33
發行號6
DOIs
出版狀態已出版 - 3 6月 2018

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