NDLI: A new hyperspectral image classification approach using fractal dimension of spectral response curve

Content Provider	IEEE Xplore Digital Library
Author	Hosseini, S.A. Ghassemian, H.
Copyright Year	2013
Description	Author affiliation: Fac. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran (Hosseini, S.A.; Ghassemian, H.)
Abstract	In classification of hyperspectral images, methods like PCA, LDA, and ICA have simple structure and relatively good results, but an important deficiency is that these methods are not sensitive to the order of primary features. For every pixel of a hyperspectral image we have a vector of measured quantities corresponding to reflection coefficients of consecutive wavelengths which is called as spectral reflectance curve (SRC). So the ordinance of measured data might have some information that could be useful in classification. SRCs due to the high degree of snuggle they have, could be considered as fractals. In this paper we suggest a new approach of fractal dimension feature extraction based on FD of adjacent overlapping intervals and using their principal components as feature vector elements. These new features applied as inputs of a statistical per pixel classifier of segmented image, produced by K-means clustering method, whit Mahalanobis distance. A majority voting step ended classification process. We achieved meaningful improvement of correct classification rate comparing to classic PCA method.
Starting Page	1
Ending Page	6
File Size	855854
Page Count	6
File Format	PDF
ISBN	9781467356343
DOI	10.1109/IranianCEE.2013.6599552
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-14
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fractal Dimension Support vector machine classification Classification Spectral reflectance curve Feature Extraction Feature extraction Educational institutions Fractals Hyperspectral imaging Principal component analysis Hyperspectral Image
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An improved spectral reflectance and derivative feature fusion for hyperspectral image classification

Classification of hyperspectral and multispectral images by using fractal dimension of spectral response curve

Hyperspectral imagery classification based on rotation invariant spectral-spatial feature

A novel approach to hyperspectral data feature extraction using rational function curve fitting

Using vector of fractal dimensions for feature reduction and phoneme recognition and classification

An unsupervised feature extractionl method for classification of hyperspectral images

Spectral Magnitude and Spectral Derivative Feature Fusion for Improved Classification of Hyperspectral Images

Domain classification using B+trees in fractal image compression

Domain classification using B+trees in fractal image compression

A new hyperspectral image classification approach using fractal dimension of spectral response curve

Similar Documents

An improved spectral reflectance and derivative feature fusion for hyperspectral image classification

Classification of hyperspectral and multispectral images by using fractal dimension of spectral response curve

Hyperspectral imagery classification based on rotation invariant spectral-spatial feature

A novel approach to hyperspectral data feature extraction using rational function curve fitting

Using vector of fractal dimensions for feature reduction and phoneme recognition and classification

An unsupervised feature extractionl method for classification of hyperspectral images

Spectral Magnitude and Spectral Derivative Feature Fusion for Improved Classification of Hyperspectral Images

Domain classification using B+trees in fractal image compression

Domain classification using B+trees in fractal image compression

A new hyperspectral image classification approach using fractal dimension of spectral response curve