Remote sensing classification method of vegetation dynamics based on time series Landsat image: a case of opencast mining area in China
作者: Jiaxing XuHua ZhaoPengcheng YinDuo JiaGang Li
作者单位: 1The National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines, China University of Mining and Technology
2Key Laboratory for Land Environment and Disaster Monitoring of National Administration of Surveying, Mapping and Geoinformation, China University of Mining and Technology
3Bureau of Land and Resources of Xuzhou
刊名: EURASIP Journal on Image and Video Processing, 2018, Vol.2018 (1), pp.1-10
来源数据库: Springer Nature Journal
DOI: 10.1186/s13640-018-0360-0
关键词: Vegetation dynamicsTime series NDVIClassificationClusteringPixel classification
英文摘要: Abstract(#br)Time series remote sensing image is an important resource for dynamic monitoring of resources and environment, and its abundant time spectrum information can be used to characterize the dynamic change of vegetation coverage. This paper proposes a comprehensive clustering and pixel classification method for extracting the vegetation dynamics based on time series Landsat normalized difference vegetation index (NDVI). This method uses the time-division algorithm for fitting time-series NDVI firstly. And the Markov random field optimized (MRF) semi-supervised dynamic time warping (DTW) kernel fuzzy c-means clustering was constructed. Then the MRF-optimized semi-supervised DTW-kernel fuzzy c-means clustering was combined with the 1-nearest neighbor (1NN) DTW pixel classification...
全文获取路径: Springer Nature  (合作)