Hybrid detectors for subpixel targets

被引:83
作者
Broadwater, Joshua
Chellappa, Rama
机构
[1] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD 20723 USA
[2] Univ Maryland, Ctr Automat Res, Dept Elect & Comp Engn, College Pk, MD 20742 USA
关键词
target detection; subspace detectors; hyperspectral data; spectral mixture models;
D O I
10.1109/TPAMI.2007.1104
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1891 / 1903
页数:13
相关论文
共 27 条
[1]  
ANDERSON GP, 1999, P IEEE AER C, V4, P177, DOI DOI 10.1109/AERO.1999.792088
[2]  
[Anonymous], 1993, P 9 THEM C GEOL REM
[3]  
ASHTON EA, 1998, PHOTOGRAMM ENG REMOT, P723
[4]   Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis [J].
Bateson, CA ;
Asner, GP ;
Wessman, CA .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2000, 38 (02) :1083-1094
[5]  
BOARDMAN JW, 1990, P SOC PHOTO-OPT INS, V1298, P222, DOI 10.1117/12.21355
[6]  
Broadwater J, 2004, INT GEOSCI REMOTE SE, P1601
[7]   Estimation of number of spectrally distinct signal sources in hyperspectral imagery [J].
Chang, CI ;
Du, Q .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (03) :608-619
[8]   Constrained subpixel target detection for remotely sensed imagery [J].
Chang, CI ;
Heinz, DC .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2000, 38 (03) :1144-1159
[9]   Hyperspectral analysis and target detection system for the Adaptive Spectral Reconnaissance Program (ASRP) [J].
Grossmann, JM ;
Bowles, J ;
Haas, D ;
Antoniades, JA ;
Grunes, MR ;
Palmadesso, P ;
Gillis, D ;
Tsang, KY ;
Baumback, M ;
Daniel, M ;
Fisher, J ;
Triandaf, I .
ALGORITHMS FOR MULTISPECTRAL AND HYPERSPECTRAL IMAGERY IV, 1998, 3372 :2-13
[10]  
HAPKE B, 1993, INTRO THEORY REFLECT, P278