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Title: Performing target specific band reduction using artificial neural networks and assessment of its efficacy using various target detection algorithms
Authors: Yadav D.
Arora M.K.
Tiwari K.C.
Ghosh J.K.
Casasent D.
Alam M.S.
Published in: Proceedings of SPIE - The International Society for Optical Engineering
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. © 2016 SPIE.
Citation: Proceedings of SPIE - The International Society for Optical Engineering, (2016)
Issue Date: 2016
Publisher: SPIE
Keywords: ANN
Dimensionality reduction
target detection
ISBN: 9781510600867
ISSN: 0277786X
Author Scopus IDs: 57190659361
Author Affiliations: Yadav, D., Department of Civil Eng., IIT Roorkee, India
Arora, M.K., Department of Civil Eng., IIT Roorkee, India
Tiwari, K.C., Civil Engineering Dept., Delhi Technological University, India
Ghosh, J.K., Department of Civil Eng., IIT Roorkee, India
Appears in Collections:Conference Publications [CE]

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