Broadwater Joshua, Chellappa Rama
Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Rd, Laurel, MD 20723, USA.
IEEE Trans Pattern Anal Mach Intell. 2007 Nov;29(11):1891-903. doi: 10.1109/TPAMI.2007.1104.
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.
亚像素检测是高光谱图像分析中的一个具有挑战性的问题。由于目标尺寸小于像素大小,检测算法必须仅依靠光谱信息。多年来已经开发了许多不同的算法来完成这项任务,但大多数探测器对该问题采用的要么是纯统计方法,要么是基于物理的方法。我们提出了两种新的混合探测器,它们通过使用物理和统计方法对背景进行建模,从而利用这些方法。结果表明,在包括多个目标、图像和区域类型的实验中,特别是在处理复杂背景中的弱目标时,与著名的AMSD和ACE亚像素算法相比,新探测器的性能有所提高。