Title :
Subpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR
Author :
Glenn, Taylor ; Dranishnikov, Dmitri ; Gader, Paul ; Zare, Alina
Author_Institution :
Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA
Abstract :
A new algorithm for subpixel target detection in hyperspectral imagery is proposed which uses the PFCM-FLICM-PCE algorithm to model and estimate the parameters of the image background. This method uses the piece-wise convex mixing model with spatial-spectral constraints, and uses possibilistic and fuzzy clustering techniques to find the piece-wise convex regions and robustly estimate the parameters. A method for integrating the elevation measurements of a co-registered LiDAR sensor is also proposed. The performance of the proposed methods is demonstrated on a real-world dataset with emplaced detection targets.
Keywords :
estimation theory; fuzzy set theory; geophysical image processing; hyperspectral imaging; image sensors; object detection; optical radar; parameter estimation; pattern clustering; PFCM-FLICM-PCE algorithm; coregistered LiDAR sensor; fuzzy clustering technique; hyperspectral imagery; parameter estimation; piecewise convex mixing model; piecewise convex spatial-spectral unmixing; possibilistic technique; robust estimation; spatial-spectral constraint; subpixel target detection; Clustering algorithms; Computational modeling; Detectors; Hyperspectral imaging; Laser radar; Object detection; context-dependent; detection; hyperspectral imaging; lidar; piece-wise convex;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6721347
