@article {12573,
	title = {Hybrid Detectors for Subpixel Targets},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {29},
	year = {2007},
	month = {2007/11//},
	pages = {1891 - 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 = {ACE subpixel algorithm;AMSD subpixel algorithm;hybrid detectors;hyperspectral imagery analysis;physics;statistics;subpixel target detection;subspace detection;object detection;spectral analysis;statistical analysis;target tracking;Algorithms;Artificial In, Automated;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, Computer-Assisted;, Computer-Assisted;Models, Computer-Assisted;Pattern Recognition, Statistical;Image Enhancement;Image Interpretation, Statistical;Numerical Analysis},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2007.1104},
	author = {Broadwater, J. and Chellapa, Rama}
}
