Subpixel Target Detection in Hyperspectral Imagery using Piece-wise Convex Spatial-Spectral Unmixing, Possibilistic and Fuzzy Clustering, and Co-Registered LIDAR

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.

 

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Plain Text:

Glenn, T.; Dranishnikov, D.; Gader, P.; Zare, A., “Subpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR,”¬†IEEE International¬†Geoscience and Remote Sensing Symposium (IGARSS), vol., no., pp. 1063-1066, July 21-26, 2013.

 

Bibtex:

@INPROCEEDINGS{Glenn:2013,
author={Glenn, T. and Dranishnikov, D. and Gader, P. and Zare, A.},
booktitle={IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
title={Subpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR},
year={2013},
month={July},
pages={1063-1066},
}

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