Sparsity-based Classification Hyperspectral Image Classification Towards Compressive Geospatial Sensing Towards Compressive Geospatial Sensing Via Fusion of LIDAR and Hyperspectral Imaging Allen Y. Yang with S. Shankar Sastry (PI) Department of EECS University of California, Berkeley yang,[email protected]GRID Workshop, 2010 The work is partially supported by ARO MURI W911NF-06-1-0076 http://www.eecs.berkeley.edu/~yang Towards Compressive Geospatial Sensing
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Sparsity-based Classification Hyperspectral Image Classification Towards Compressive Geospatial Sensing
Towards Compressive Geospatial SensingVia Fusion of LIDAR and Hyperspectral Imaging
Allen Y. Yangwith S. Shankar Sastry (PI)
Department of EECSUniversity of California, Berkeleyyang,[email protected]
GRID Workshop, 2010
The work is partially supported by ARO MURI W911NF-06-1-0076
http://www.eecs.berkeley.edu/~yang Towards Compressive Geospatial Sensing
Sparsity-based Classification Hyperspectral Image Classification Towards Compressive Geospatial Sensing
Challenges in Geospatial Representation and Compression
Modern geospatial databases contain large amounts of multimodal data.
Traditionally, each sensing modality is compressed independently.
In particular, geometric compression of LIDAR point clouds depends on decomposition ofcoarse surface components [Samet & Kochut 2002, Wang & Tseng 2004, McDaniel et al. 2010].
Figure: Point Scatters, Lines, Planes.
Such decomposition by LIDAR points alone is a chichen-and-egg problem.