NDLI: An enhanced density peak-based clustering approach for hyperspectral band selection

Content Provider	IEEE Xplore Digital Library
Author	Guihua Tang Sen Jia Jun Li
Copyright Year	2015
Description	Author affiliation: Coll. of Comput. Sci. & Software Eng., Shenzhen Univ., Shenzhen, China (Guihua Tang; Sen Jia) \|\| Sch. of Geogr. & Planning, Sun Yat-sen Univ., Guangzhou, China (Jun Li)
Abstract	Recently, a fast density peak-based clustering algorithm, namely FDPC, has demonstrated its power on nonspherical clustering problems. In this paper, we propose an enhanced fast density peak-based clustering, namely E-FDPC, for hy-perspectral band selection. The main contributions of the proposed E-FDPC, in comparison with the original FDPC are two folds. First, we introduce a parameter to control the weight between the normalized local density and intra-cluster distance. The other aspect is that, we present an exponential-based learning rule to adjust the cut-off threshold for different number of selected bands, where it is empirically defined in FDPC. Furthermore, an effective strategy, called isolated-point-stopping criterion, is developed to automatically determine the appropriate number of bands. That is, the clustering process will be stopped by the emergence of the isolated point (the only point in one cluster). Experimental results on real hyperspectral data demonstrate that E-FDPC approach could achieve higher overall classification accuracies than FDPC and other state-of-the-art band selection techniques.
Starting Page	1116
Ending Page	1119
File Size	279850
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7325966
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Accuracy Clustering algorithms Training Feature extraction density-based clustering Hyperspectral imagery band selection
Content Type	Text
Resource Type	Article

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