Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier

NCUIR > Research Center > Center for Space and Remote Sensing Research > journal & Dissertation > Item 987654321/50645

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50645

Title:	Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier
Authors:	Chen,CF;Son,NT;Chen,CR;Chang,LY
Contributors:	太空及遙測研究中心
Keywords:	FOURIER-ANALYSIS;AGRICULTURE;PHENOLOGY;SYSTEMS
Date:	2011
Issue Date:	2012-03-27 17:49:51 (UTC+8)
Publisher:	國立中央大學
Abstract:	Rice is the most important economic crop in Vietnam's Mekong Delta (MD). It is the main source of employment and income for rural people in this region. Yearly estimates of rice growing areas and delineation of spatial distribution of rice crops are needed to devise agricultural economic plans and to ensure security of the food supply. The main objective of this study is to map rice cropping systems with respect to monitoring agricultural practices in the MD using time-series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250-m data. These time-series NDVI data were derived from the 8-day MODIS 250-m data acquired in 2008. Various spatial and nonspatial data were also used for accuracy verification. The method used in this study consists of the following three main steps: 1. filtering noise from the time-series NDVI data using wavelet transformation (Coiflet 4); 2. classification of rice cropping systems using parametric and nonparametric classification algorithms: the maximum likelihood classifier (MLC) and support vector machines (SVMs); and 3. verification of classification results using ground truth data and government rice statistics. Good results can be found using wavelet transformation for cleaning rice signatures. The results of classification accuracy assessment showed that the SVMs outperformed the MLC. The overall accuracy and Kappa coefficient achieved by the SVMs were 89.7% and 0.86, respectively, while those achieved by the MLC were 76.2% and 0.68, respectively. Comparison of the MODIS-derived areas obtained by the SVMs with the government rice statistics at the provincial level also demonstrated that the results achieved by the SVMs (R(2) = 0.95) were better than the MLC (R(2) = 0.91). This study demonstrates the effectiveness of using a nonparametric classification algorithm (SVMs) and time-series MODIS NVDI data for rice crop mapping in the Vietnamese MD. (C) 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3595272]
Relation:	JOURNAL OF APPLIED REMOTE SENSING
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	710	View/Open

社群 sharing

Loading...