NDLI: A novel hyperspectral image clustering method based on spectral unmixing

Content Provider	IEEE Xplore Digital Library
Author	Gholizadeh, H. Zoej, M.J.V. Mojaradi, B.
Copyright Year	2012
Description	Author affiliation: School of Civil Engineering, Iran University of Science and Technology, Tehran, P.O. Box 16765-163 (Mojaradi, B.) \|\| Faculty of Geodesy and Geomatics, Engineering, K.N. Toosi University of Technology, Vali-Asr St., Mirdamad Crossing, Tehran, P.O. Box: 15875-4416 (Gholizadeh, H.) \|\| Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Vali-Asr St., Mirdamad Crossing, Tehran, P.O. Box: 15875-4416 (Zoej, M.J.V.)
Abstract	In this paper, a novel hyperspectral image clustering procedure, which is based upon the Fully Constrained Least Squares (FCLS) spectral unmixing method, is proposed. The proposed clustering method consists of three major steps: endmember extraction, unmixing procedure and hardening process via the winner-takes-all approach. To estimate the optimal number of endmembers, instead of using the background signal subspace identification methods, the number of endmembers is varied in a predefined interval and the commonly accepted VCA (Vertex Component Analysis) algorithm is employed to extract the endmembers' spectra. At each iteration, the bandwise Root Mean Square Error (RMSE) between the reconstructed image, obtained from estimated fractions. and the original image is computed and the mean of all bandwise RMSEs is regarded as a measure to choose the optimum number of endmembers. Experiments conducted on the Indian Pines challenging dataset proved the superiority of proposed method over the K-Means and Fuzzy c-Means methods in terms of the widely used Adjusted Rand Index measure.
Starting Page	1
Ending Page	5
File Size	794276
Page Count	5
File Format	PDF
ISBN	9781457705564
ISSN	1095323X
e-ISBN	9781457705571
DOI	10.1109/AERO.2012.6187196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Educational institutions Clustering methods Hyperspectral imaging Image reconstruction Clustering algorithms
Content Type	Text
Resource Type	Article
Subject	Aerospace Engineering Space and Planetary Science

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