DocumentCode
1120332
Title
Hybrid Detectors for Subpixel Targets
Author
Broadwater, Joshua ; Chellappa, Rama
Author_Institution
Johns Hopkins Univ., Laurel
Volume
29
Issue
11
fYear
2007
Firstpage
1891
Lastpage
1903
Abstract
Subpixel detection is a challenging problem in hyperspectral imagery analysis. Since the target size is smaller than the size of a pixel, detection algorithms must rely solely on spectral information. A number of different algorithms have been developed over the years to accomplish this task, but most detectors have taken either a purely statistical or a physics-based approach to the problem. We present two new hybrid detectors that take advantage of these approaches by modeling the background using both physics and statistics. Results demonstrate improved performance over the well-known AMSD and ACE subpixel algorithms in experiments that include multiple targets, images, and area types - especially when dealing with weak targets in complex backgrounds.
Keywords
object detection; spectral analysis; statistical analysis; target tracking; ACE subpixel algorithm; AMSD subpixel algorithm; hybrid detectors; hyperspectral imagery analysis; physics; statistics; subpixel target detection; subspace detection; Array signal processing; Building materials; Covariance matrix; Detection algorithms; Detectors; Hyperspectral imaging; Least squares methods; Pixel; Testing; Vectors; Target detection; hyperspectral data; spectral mixture models; subspace detectors; Algorithms; Artificial Intelligence; Computer Graphics; Computer Simulation; Data Interpretation, Statistical; Image Enhancement; Image Interpretation, Computer-Assisted; Models, Statistical; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2007.1104
Filename
4302756
Link To Document