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

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)587-601
Number of pages15
JournalGeocarto International
Volume33
Issue number6
DOIs
StatePublished - 3 Jun 2018

Keywords

  • Sentinel-1A data
  • random forests
  • rice-cropping systems
  • support vector machines

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