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Mean shift-based clustering of remotely sensed data with agricultural and land-cover applications
Mean shift-based clustering of remotely sensed data with agricultural and land-cover applications
- Source :
- International Journal of Remote Sensing. 34:6037-6053
- Publication Year :
- 2013
- Publisher :
- Informa UK Limited, 2013.
-
Abstract
- The mean shift MS algorithm is based on a statistical approach to the clustering problem. Specifically, the method is a variant of density estimation. We revisit in this article the MS paradigm and its use for clustering of remotely sensed images. Specifically, we investigate further the classification accuracy of remotely sensed images as a function of various MS parameters, such as the variant used, kernel type, dimensionality, kernel bandwidth, etc. We provide empirical assessment of the algorithm based on experiments with multi-temporal and multi-spectral remotely sensed data sets, representing agricultural and land-cover data. Although the classification accuracy and reliability seem comparable to those obtained by other unsupervised methods e.g. ISODATA, the MS algorithm provides several important operational advantages. The adaptation of the procedure to a parallel computational environment is also discussed and demonstrated.
- Subjects :
- business.industry
Computer science
Pattern recognition
Land cover
Density estimation
Kernel Bandwidth
computer.software_genre
Multispectral pattern recognition
Kernel (image processing)
General Earth and Planetary Sciences
Data mining
Artificial intelligence
Mean-shift
business
Cluster analysis
computer
Curse of dimensionality
Subjects
Details
- ISSN :
- 13665901 and 01431161
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- International Journal of Remote Sensing
- Accession number :
- edsair.doi...........291d7c929add4bd462f226fbb0da719e
- Full Text :
- https://doi.org/10.1080/01431161.2013.793866