Pixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari

Pixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari

Dr. Farooq Ahmad

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University of the Punjab

Pixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari

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Abstract

The hyperspectral image analysis technique, one of the most advanced remote sensing tools, has been used as a possible means of identifying from a single pixel or in the field of view of the sensor. An important problem in hyperspectral image processing is to decompose the mixed pixels into the information that contribute to the pixel, endmember, and a set of corresponding fractions of the spectral signature in the pixel, abundances, and this problem is known as un-mixing. The effectiveness of the hyperspectral image analysis technique used in this study lies in their ability to compare a pixel spectrum with the spectra of known pure vegetation, extracted from the spectral endmember selection procedures, including the reflectance calibration of Landsat ETM+ image using ENVI software, minimum noise fraction (MNF), pixel purity index (PPI), and n-dimensional visualization. The Endmember extraction is one of the most fundamental and crucial tasks in hyperspectral data exploitation, an ultimate goal of an endmember extraction algorithm is to find the purest form of spectrally distinct resource information of a scene.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Dr. Farooq Ahmad. 2013. \u201cPixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari\u201d. Global Journal of Human-Social Science - A: Arts & Humanities GJHSS-A Volume 12 (GJHSS Volume 12 Issue A15): .

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GJHSS Volume 12 Issue A15
Pg. 23- 32

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Journal Specifications

Crossref Journal DOI 10.17406/GJHSS

Print ISSN 0975-587X

e-ISSN 2249-460X

Keywords

algorithm endmember fraction LandsatMNF n-dimension PPI Remote Sensing

Submission ReceivedDecember 14, 2011
Peer Review Double Blind
Handling Editor
Accepted January 1, 2012
Published January 15, 2012

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v1.2

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January 12, 2013

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Total Score: 106

Country: Pakistan

Subject: Global Journal of Human-Social Science - A: Arts & Humanities

Authors: Dr. Farooq Ahmad (PhD/Dr. count: 1)

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