Unsupervised spectral-spatial feature selection-based camouflaged object detection using VNIR hyperspectral camera

Title
Unsupervised spectral-spatial feature selection-based camouflaged object detection using VNIR hyperspectral camera
Author(s)
김성호
Keywords
Article; camera; entropy; human; online analysis; optics; spatial analysis; spectroscopy
Issue Date
201503
Citation
Scientific World Journal, v.2015
Abstract
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. ? 2015 Sungho Kim.
URI
http://hdl.handle.net/YU.REPOSITORY/33010http://dx.doi.org/10.1155/2015/834635
ISSN
2356-6140
Appears in Collections:
공과대학 > 전자공학과 > Articles
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