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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

Authors :
Lior Friedman
Nathan S. Netanyahu
Maxim Shoshany
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.

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