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Reseach Article

Maximum Distance Band Selection of Hyperspectral Images

by Shahram Latifi, Steven Wilson
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 17
Year of Publication: 2016
Authors: Shahram Latifi, Steven Wilson
10.5120/ijca2016908214

Shahram Latifi, Steven Wilson . Maximum Distance Band Selection of Hyperspectral Images. International Journal of Computer Applications. 133, 17 ( January 2016), 36-43. DOI=10.5120/ijca2016908214

@article{ 10.5120/ijca2016908214,
author = { Shahram Latifi, Steven Wilson },
title = { Maximum Distance Band Selection of Hyperspectral Images },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 17 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 36-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number17/24009-2016908214/ },
doi = { 10.5120/ijca2016908214 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:32:49.570920+05:30
%A Shahram Latifi
%A Steven Wilson
%T Maximum Distance Band Selection of Hyperspectral Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 17
%P 36-43
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hyperspectral Imaging has been advanced by recent improvements in airborne imaging hardware. Early airborne HSI datasets such as Indian Pines, have a relatively low spatial and spectral resolution and are useful primarily for research purposes. Higher resolution and lower sensor noise has become the industry standard. Since there is more high quality data available, less emphasis can be placed on denoising and pixel unmixing, and the problem becomes one of computational complexity. Therefore, there is a need for preprocessing methods which reduce the amount of raw data processed by target detection algorithms. The purpose of this research is to propose a method of maximum distance automated band selection in order to preprocess hyperspectral image cube data, and present the results when compared to those using the entire data set. The goal is to significantly increase the accuracy of target detection using a Robust Matched Filter (RMF) while at the same time reducing the computational time required to process the data.

References
  1. Purdue Research Foundation, "Aviris image Indian Pine Test Site," 2014, https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html.
  2. Caltech, "AVIRIS Overview," 2000, http://aviris.jpl.nasa.gov/html/overview.html.
  3. SpecTIR Remote Sensing Division, "ProSpecTIR VS," 2011, http://www.spectir.com/wp-content/uploads/2012/02/ProSpecTIR_VS_specs_2011.pdf.
  4. Rochester Institute of Technology, Flexviewer, http://barracuda.cis.rit.edu/flexviewer/.
  5. K. N. Liou, An introduction to atmospheric radiation. vol. 84. Academic press, 2002. page 86
  6. L. Wang, Y. Zhang, and Y. Gu, " Unsupervised Band Selection Method Based on Improved N-FINDR Algorithm for Spectral Unmixing," ISSCAA Preceedings pp.10211024,2006.http://dx.doi.org/10.1109/ISSCAA.2006.1627496
  7. BS ISO 5725-1: "Accuracy (trueness and precision) of measurement methods and results - Part 1: General principles and definitions.", p.1 (1994)
Index Terms

Computer Science
Information Sciences

Keywords

hyperspectral imaging automated standoff target detection