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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5211
Title: Objects Detection in Hyperspectral Images Using Spectral Derivative
Authors: Kumar V.
Ghosh, Jayanta Kumar
Published in: Journal of the Indian Society of Remote Sensing
Abstract: In recent years hyperspectral imaging has proved its significance in the detection and mapping of various objects of interest in a scene. Various methods for object detection in hyperspectral images have been developed with their advantages and limitations. In the present study, a methodology comprising spectral derivative (first order) and spectral information divergence has been investigated for detection of objects in hyperspectral images. The efficacy of the detection scheme has been examined over two different hyperspectral data sets of Hyperion images. Tea plants (Camellia sinensis) and Sal trees (Shorea robusta) (pure pixels) have been detected as the objects of interest in the hyperspectral images independently with reduced false pixels. The proposed methodology may in future be applied for classification of mixed pixels. ¬© 2016, Indian Society of Remote Sensing.
Citation: Journal of the Indian Society of Remote Sensing(2017), 45(4): 603-610
URI: https://doi.org/10.1007/s12524-016-0627-9
http://repository.iitr.ac.in/handle/123456789/5211
Issue Date: 2017
Publisher: Springer India
Keywords: Hyperspectral imaging
Spectral derivative
Spectral information divergence
ISSN: 0255660X
Author Scopus IDs: 36986501300
34968946700
Author Affiliations: Kumar, V., Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Ghosh, J.K., Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Corresponding Author: Kumar, V.; Department of Civil Engineering, Indian Institute of Technology RoorkeeIndia; email: vksaharavat@gmail.com
Appears in Collections:Journal Publications [CE]

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