Name
Abstract | ||
|---|---|---|
Normalized difference vegetation index (NDVI) is a very important vegetation index, which has been widely applied in research regarding global environmental and climatic change. In this work, 16-Day L3 Global 1 km SIN Grid NDVI data sets in Heihe River Basin from MODIS vegetation index (VI) products (MOD13A2) during 2003-2005 are extracted and used for generating a one-year new NDVI data based on a simple three-point smoothing technique which can generally capture the annual feature of vegetation change. Then we obtain the independent component images by performing independent component analysis (ICA) transform on the smoothing NDVI data as a feature extractor. Then a support vector machine (SVM) is utilized to construct classifiers based on the ICA-extracted new features for land cover classification and a land cover map of Heihe river basin was obtained. At last, the accuracy assessment results prove that the classification framework proposed in this paper is efficient. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FSKD.2008.517 | FSKD (2) |
Keywords | Field | DocType |
vegetation change,modis vegetation index,sin grid ndvi data,three-point smoothing technique,modis ndvi data,normalized difference vegetation index,annual feature,one-year new ndvi data,feature extractor,svm,ica-extracted new feature,ndvi,independent component analysis,smoothing ndvi data,land cover classification,heihe river basin,feature extraction,support vector machine,time series,classification framework,vegetation mapping,important vegetation index,ica,support vector machines,indexes,river basin,climate change,remote sensing | Vegetation,Drainage basin,Pattern recognition,Computer science,Remote sensing,Support vector machine,Feature extraction,Smoothing,Normalized Difference Vegetation Index,Artificial intelligence,Enhanced vegetation index,Land cover | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3305-6 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Gu | 1 | 14 | 3.98 |
| Xin Li | 2 | 749 | 118.91 |
| Chunlin Huang | 3 | 36 | 7.22 |