Research Papers

Hyperspectral image classification using an unsupervised neuro-fuzzy system

[+] Author Affiliations
Caiyun Zhang

Florida Atlantic University, Department of Geosciences, 777 Glades Road, Boca Raton, Florida 33431

Fang Qiu

University of Texas at Dallas, Program in Geospatial Information Sciences, 800 West Campbell Road, Richardson, Texas 75080

J. Appl. Remote Sens. 6(1), 063515 (Apr 06, 2012). doi:10.1117/1.JRS.6.063515
History: Received October 10, 2011; Accepted February 15, 2012
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Abstract.  An unsupervised neuro-fuzzy system, Gaussian fuzzy self-organizing map (GFSOM), is proposed for hyperspectral image classification. This algorithm operates by integrating an unsupervised neural network with a Gaussian function-based fuzzy system. We also explore the potential for hyperspectral image analysis of three other artificial intelligence (AI)-based unsupervised techniques popular for multispectral image analysis: self-organizing map (SOM), fuzzy c-mean (FCM), and descending fuzzy learning vector quantization (DFLVQ). To apply these methods effectively and efficiently to hyperspectral imagery, an optimal learning sample selection strategy and a prototype initialization system are developed. An experimental study on classifying an EO-1/Hyperion hyperspectral image illustrates that GFSOM achieves the best accuracy, since it can model both the central tendency characteristics of input samples and capture the dispersion characteristics of data within a cluster. By adopting the system initialization approach developed here, all the AI-based techniques have the capability to classify hyperspectral images and can deliver acceptable accuracy, which could consequently accelerate their transitions from the multispectral to the hyperspectral field.

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© 2012 Society of Photo-Optical Instrumentation Engineers


Caiyun Zhang and Fang Qiu
"Hyperspectral image classification using an unsupervised neuro-fuzzy system", J. Appl. Remote Sens. 6(1), 063515 (Apr 06, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063515

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