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Remote sensing classification method of vegetation dynamics based on time series Landsat image: a case of opencast mining area in China
- Source :
- EURASIP Journal on Image and Video Processing, Vol 2018, Iss 1, Pp 1-10 (2018)
- Publication Year :
- 2018
- Publisher :
- SpringerOpen, 2018.
-
Abstract
- 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 to realize the extraction of vegetation dynamics. Shengli Opencast Coal Mine in The Xilin Gol Grassland was taken as the study area to analyze the applicability of the different classification methods. The results showed the fusion algorithm of the MRF-Semi-GDTW-FCM and 1NN-DTW generates accurate classification results with the overall accuracy of 93.8806% and Kappa coefficient of 0.9267, which were 1.7219, 0.0182, and 20.4080% and 0.2916 higher than the clustering and pixel classification, respectively. Experiments proof that the method proposed in this paper is not only simple but also accurate and effective.
- Subjects :
- Dynamic time warping
010504 meteorology & atmospheric sciences
Biometrics
Time series NDVI
Computer science
0211 other engineering and technologies
lcsh:TK7800-8360
02 engineering and technology
01 natural sciences
Fuzzy logic
Normalized Difference Vegetation Index
Clustering
Vegetation dynamics
Pixel classification
Cohen's kappa
Electrical and Electronic Engineering
Cluster analysis
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Markov random field
lcsh:Electronics
Classification
Kernel (image processing)
Signal Processing
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 16875281
- Volume :
- 2018
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- EURASIP Journal on Image and Video Processing
- Accession number :
- edsair.doi.dedup.....af4b27459dc1cab25b0d21679426e00d