Fast algorithm for exploring and compressing of large hyperspectral images

Sergey Kucheryavskiy

doi:10.1109/WHISPERS.2011.6080850

Fast algorithm for exploring and compressing of large hyperspectral images

Department of Chemistry and Bioscience

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Abstract

A new method for calculation of latent variable space for exploratory analysis and dimension reduction of large hyperspectral images is proposed. The method is based on significant downsampling of image pixels with preservation of pixels’ structure in feature (variable) space. To achieve this, information about pixels density in principal component space for the first two components is utilized. The method was tested on several hyperspectral images and showed significant improvement of performance while the orientation of the latent variables was not very different from the original one. The method can be used first of all for fast compression of large data arrays with principal component analysis or similar projection techniques.

Original language	English
Title of host publication	3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2011
Number of pages	4
Publisher	IEEE
Publication date	6 Jun 2011
DOIs	https://doi.org/10.1109/WHISPERS.2011.6080850
Publication status	Published - 6 Jun 2011
Event	WHISPERS 2011 - Lisbon, Portugal Duration: 6 Jun 2011 → 9 Jun 2011

Workshop

Workshop	WHISPERS 2011
Country/Territory	Portugal
City	Lisbon
Period	06/06/2011 → 09/06/2011

Keywords

Hyperspectral data
Principal component analysis
Latent variables
Data compressing

Access to Document

10.1109/WHISPERS.2011.6080850

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6080850

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Fast algorithm for exploring and compressing of large hyperspectral images
Sergey Kucheryavskiy (Speaker)
6 Jun 2011 → 9 Jun 2011
Activity: Talks and presentations › Talks and presentations in private or public companies

Cite this

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title = "Fast algorithm for exploring and compressing of large hyperspectral images",

abstract = "A new method for calculation of latent variable space for exploratory analysis and dimension reduction of large hyperspectral images is proposed. The method is based on significant downsampling of image pixels with preservation of pixels{\textquoteright} structure in feature (variable) space. To achieve this, information about pixels density in principal component space for the first two components is utilized. The method was tested on several hyperspectral images and showed significant improvement of performance while the orientation of the latent variables was not very different from the original one. The method can be used first of all for fast compression of large data arrays with principal component analysis or similar projection techniques.",

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AB - A new method for calculation of latent variable space for exploratory analysis and dimension reduction of large hyperspectral images is proposed. The method is based on significant downsampling of image pixels with preservation of pixels’ structure in feature (variable) space. To achieve this, information about pixels density in principal component space for the first two components is utilized. The method was tested on several hyperspectral images and showed significant improvement of performance while the orientation of the latent variables was not very different from the original one. The method can be used first of all for fast compression of large data arrays with principal component analysis or similar projection techniques.

KW - Hyperspectral data

KW - Principal component analysis

KW - Latent variables

KW - Data compressing

U2 - 10.1109/WHISPERS.2011.6080850

DO - 10.1109/WHISPERS.2011.6080850

M3 - Article in proceeding

BT - 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2011

PB - IEEE

T2 - WHISPERS 2011

Y2 - 6 June 2011 through 9 June 2011

ER -

Fast algorithm for exploring and compressing of large hyperspectral images

Abstract

Workshop

Keywords

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Fast algorithm for exploring and compressing of large hyperspectral images

Cite this