Hyperspectral image classification using an unsupervised neuro-fuzzy system

Caiyun Zhang; Fang Qiu

doi:10.1117/1.JRS.6.063515

6 April 2012 Hyperspectral image classification using an unsupervised neuro-fuzzy system

Caiyun Zhang, Fang Qiu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 6, Issue 1, 063515 (April 2012). https://doi.org/10.1117/1.JRS.6.063515

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.

Citation Download Citation

Caiyun Zhang and Fang Qiu "Hyperspectral image classification using an unsupervised neuro-fuzzy system," Journal of Applied Remote Sensing 6(1), 063515 (6 April 2012). https://doi.org/10.1117/1.JRS.6.063515

Published: 6 April 2012

ACCESS THE FULL ARTICLE

JOURNAL ARTICLE
15 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY