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Comparative Study of Intrinsic Dimensionality Estimation and Dimension Reduction Techniques on Hyperspectral Images Using K-NN Classifier

Samadzadegan, Farhad and Hasanlou, Mahdi (2012) Comparative Study of Intrinsic Dimensionality Estimation and Dimension Reduction Techniques on Hyperspectral Images Using K-NN Classifier. IEEE Geoscience and Remote Sensing Letters, 9 (6), pp. 1046-1050. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/LGRS.2012.2189547 ISSN 1545-598X

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Abstract

Nowadays, hyperspectral remote sensors are readily available for monitoring the Earth’s surface with high spectral resolution. The high-dimensional nature of the data collected by such sensors not only increases computational complexity but also can degrade classification accuracy. To address this issue, dimensionality reduction (DR) has become an important aid to improving classifier efficiency on these images. The common approach to decreasing dimensionality is feature extraction by considering the intrinsic dimensionality (ID) of the data. A wide range of techniques for ID estimation (IDE) and DR for hyperspectral images have been presented in the literature. However, the most effective and optimum methods for IDE and DR have not been determined for hyperspectral sensors, and this causes ambiguity in selecting the appropriate techniques for processing hyperspectral images. In this letter, we discuss and compare ten IDE and six DR methods in order to investigate and compare their performance for the purpose of supervised hyperspectral image classification by using K-nearest neighbor (K-NN). Due to the nature of K-NN classifier that uses different distance metrics, a variety of distance metrics were used and compared in this procedure. This letter presents a review and comparative study of techniques used for IDE and DR and identifies the best methods for IDE and DR in the context of hyperspectral image analysis. The results clearly show the superiority of the hyperspectral signal subspace identification by minimum, second moment linear, and noise-whitened Harsanyi–Farrand–Chang estimators, also the principal component analysis and independent component analysis as DR techniques, and the norm L1 and Euclidean distance metrics to process hyperspectral imagery by using the K-NN classifier.

Item URL in elib:https://elib.dlr.de/78528/
Document Type:Article
Title:Comparative Study of Intrinsic Dimensionality Estimation and Dimension Reduction Techniques on Hyperspectral Images Using K-NN Classifier
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Samadzadegan, Farhadfarhad.samadzadegan (at) dlr.deUNSPECIFIED
Hasanlou, MahdiUNSPECIFIEDUNSPECIFIED
Date:November 2012
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:9
DOI :10.1109/LGRS.2012.2189547
Page Range:pp. 1046-1050
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:Published
Keywords:Dimension reduction, distance metric, feature extraction, hyperspectral images, intrinsic dimension estimation (IDE),K-nearest neighbor (K-NN) classifier
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Reinartz, Prof. Dr.. Peter
Deposited On:14 Nov 2012 14:26
Last Modified:08 Mar 2018 18:30

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