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  2/2017 - 13

Three-Dimensional Spatial-Spectral Filtering Based Feature Extraction for Hyperspectral Image Classification

AKYUREK, H. A. See more information about AKYUREK, H. A. on SCOPUS See more information about AKYUREK, H. A. on IEEExplore See more information about AKYUREK, H. A. on Web of Science, KOCER, B. See more information about KOCER, B. on SCOPUS See more information about KOCER, B. on SCOPUS See more information about KOCER, B. on Web of Science
 
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Author keywords
adaptive algorithms, feature extraction, gaussian noise, hyperspectral imaging, image classification

References keywords
image(26), hyperspectral(24), sensing(22), remote(22), classification(22), geoscience(13), images(10), tgrs(9), analysis(9), preserving(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 95 - 102
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02013
Web of Science Accession Number: 000405378100013
SCOPUS ID: 85020078642

Abstract
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Hyperspectral pixels which have high spectral resolution are used to predict decomposition of material types on area of obtained image. Due to its multidimensional form, hyperspectral image classification is a challenging task. Hyperspectral images are also affected by radiometric noise. In order to improve the classification accuracy, many researchers are focusing on the improvement of filtering, feature extraction and classification methods. In the context of hyperspectral image classification, spatial information is as important as spectral information. In this study, a three-dimensional spatial-spectral filtering based feature extraction method is presented. It consists of three main steps. The first is a pre-processing step which include spatial-spectral information filtering in three-dimensional space. The second comprises extract functional features of filtered data. The last one is combining extracted features by serial feature fusion strategy and using to classify hyperspectral image pixels. Experiments were conducted on two popular public hyperspectral remote sensing image, 1%, 5%, 10% and 15% of samples of each classes used as training set, the remaining is used as test set. The proposed method compared with well-known methods. Experimental results show that the proposed method achieved outstanding performance than compared methods in hyperspectral image classification task.


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References Weight

Web of Science® Citations for all references: 46,247 TCR
SCOPUS® Citations for all references: 49,557 TCR

Web of Science® Average Citations per reference: 984 ACR
SCOPUS® Average Citations per reference: 1,054 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-10-15 06:33 in 275 seconds.




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