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.

Links:

Citation:

T. Glenn, D. Dranishnikov, P. Gader, and A. Zare, “Subpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR,” in IEEE Int. Geoscience and Remote Sens. Symp.(IGARSS)), 2013, pp. 1063-1066. 

@InProceedings{glenn2013subpixel,
Title = {Subpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR},
Author = {Taylor Glenn and Dmitri Dranishnikov and Paul Gader and Alina Zare},
Booktitle = {IEEE Int. Geoscience and Remote Sens. Symp.(IGARSS))},
Year = {2013},
Month = {July},
Pages = {1063-1066},
Doi = {10.1109/IGARSS.2013.6721347},
}