Unsupervised Spectral-Spatial Feature Selection-Based Camouflaged Object Detection Using VNIR Hyperspectral Camera
Author
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-23
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The detection of camouflaged objects is important for industrial inspection, medical diagnoses, and military applications.
Conventional supervised learning methods for hyperspectral images can be a feasible solution.
Such approaches, however, require a priori information of a camouflaged object and background.
This letter proposes a fully autonomous feature selection and camouflaged object detection method based on the online analysis of spectral and spatial features.
The statistical distance metric can generate candidate feature bands and further analysis of the entropy-based spatial grouping property can trim the useless feature bands.
Camouflaged objects can be detected better with less computational complexity by optical spectral-spatial feature analysis.
American Psychological Association (APA)
Kim, Sungho. 2015. Unsupervised Spectral-Spatial Feature Selection-Based Camouflaged Object Detection Using VNIR Hyperspectral Camera. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1079217
Modern Language Association (MLA)
Kim, Sungho. Unsupervised Spectral-Spatial Feature Selection-Based Camouflaged Object Detection Using VNIR Hyperspectral Camera. The Scientific World Journal No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1079217
American Medical Association (AMA)
Kim, Sungho. Unsupervised Spectral-Spatial Feature Selection-Based Camouflaged Object Detection Using VNIR Hyperspectral Camera. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1079217
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1079217