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Proceedings Paper

Performing target specific band reduction using artificial neural networks and assessment of its efficacy using various target detection algorithms
Author(s): Deepti Yadav; M. K. Arora; K. C. Tiwari; J. K. Ghosh
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Paper Abstract

Hyperspectral imaging is a powerful tool in the field of remote sensing and has been used for many applications like mineral detection, detection of landmines, target detection etc. Major issues in target detection using HSI are spectral variability, noise, small size of the target, huge data dimensions, high computation cost, complex backgrounds etc. Many of the popular detection algorithms do not work for difficult targets like small, camouflaged etc. and may result in high false alarms. Thus, target/background discrimination is a key issue and therefore analyzing target’s behaviour in realistic environments is crucial for the accurate interpretation of hyperspectral imagery. Use of standard libraries for studying target’s spectral behaviour has limitation that targets are measured in different environmental conditions than application. This study uses the spectral data of the same target which is used during collection of the HSI image. This paper analyze spectrums of targets in a way that each target can be spectrally distinguished from a mixture of spectral data. Artificial neural network (ANN) has been used to identify the spectral range for reducing data and further its efficacy for improving target detection is verified. The results of ANN proposes discriminating band range for targets; these ranges were further used to perform target detection using four popular spectral matching target detection algorithm. Further, the results of algorithms were analyzed using ROC curves to evaluate the effectiveness of the ranges suggested by ANN over full spectrum for detection of desired targets. In addition, comparative assessment of algorithms is also performed using ROC.

Paper Details

Date Published: 20 April 2016
PDF: 14 pages
Proc. SPIE 9845, Optical Pattern Recognition XXVII, 984507 (20 April 2016); doi: 10.1117/12.2224429
Show Author Affiliations
Deepti Yadav, Indian Institute of Technology Roorkee (India)
M. K. Arora, Indian Institute of Technology Roorkee (India)
K. C. Tiwari, Delhi Technological Univ. (India)
J. K. Ghosh, Indian Institute of Technology Roorkee (India)

Published in SPIE Proceedings Vol. 9845:
Optical Pattern Recognition XXVII
David Casasent; Mohammad S. Alam, Editor(s)

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