Paper Info
Title
Geodesic based hybrid similarity criteria for approximate spectral clustering of remote sensing images
Abstract
Spectral clustering has been successfully used in many applications thanks to its ability to extract clusters with various characteristics without a parametric model and its easy implementation. However, due to its computational cost and memory requirement, it is infeasible for big data such as remote sensing images and it can only be applied through data representatives (obtained by quantization). This approach, approximate spectral clustering (ASC), not only exploits the advantages of spectral clustering for big data, but also enables representing detailed data characteristics in different aspects including topology, local density distribution, Euclidean or geodesic distance. This study presents geodesic based hybrid similarity criteria harnessing different types of information for ASC and shows their performance in extraction of agricultural lands.
Year
DOI
Venue
2014
10.1109/SIU.2014.6830297
Signal Processing and Communications Applications Conference
Keywords
DocType
ISSN
agriculture,differential geometry,geophysical image processing,remote sensing,approximate spectral clustering,geodesic based hybrid similarity criteria,remote sensing images,CONN similarity,agriculture,approximate spectral clustering,big data,geodesic based similarity,remote sensing
Conference
2165-0608
Citations 
PageRank 
References 
0
0.34
14
Authors
2
Name
Order
Citations
PageRank
Yaser Moazzen141.77
Kadim Tasdemir222417.30