Band Selection of Hyperspectral Imagery Using a Weighted Fast Density Peak-Based Clustering Approach View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Sen Jia , Guihua Tang , Jie Hu

ABSTRACT

Based on the search strategy of representative bands in Hyperspectral Imagery, various existing unsupervised band selection approaches are mainly classified into two parts: ranking-based and clustering-based ones. Recently, a fast density peak-based clustering (abbreviated as FDPC) algorithm has been proposed. The product of two factors (the computation of local density and intra-cluster distance) is sorted in decreasing order and cluster centers are recognized as points with anomalously large values, hence the FDPC algorithm can be considered as a ranking-based clustering method. In this paper, the FDPC algorithm has been modified to make it suitable for hyperspectral band selection by weighting the normalized local density and intra-cluster distance. It is called a weighted fast density peak-based clustering (W-FDPC) method. Experimental results demonstrate that the bands selected by W-FDPC approach can achieve higher overall classification accuracies than FDPC and other state-of-the-art band selection techniques. More... »

PAGES

50-59

References to SciGraph publications

Book

TITLE

Intelligence Science and Big Data Engineering. Image and Video Data Engineering

ISBN

978-3-319-23987-3
978-3-319-23989-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-23989-7_6

DOI

http://dx.doi.org/10.1007/978-3-319-23989-7_6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015961706


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