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Library 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

Mean shift-based clustering of remotely sensed data with agricultural and land-cover applications

Resource information

Date of publication
december 2013
Resource Language
ISBN / Resource ID
AGRIS:US201400169018
Pages
6037-6053

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.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Friedman, Lior
Netanyahu, Nathan S.
Shoshany, Maxim

Publisher(s)
Data Provider