TY  - CONF
AU  - M. Campos-Taberner
AU  - Adriana Romero
AU  - Carlo Gatta
AU  - Gustavo Camps-Valls
A2  - IGARSS
PY  - 2015//
TI  - Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination
BT  - IEEE International Geoscience and Remote Sensing Symposium IGARSS2015
SP  - 4169
EP  - 4172
N2  - This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization.
UR  - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7303999
UR  - http://dx.doi.org/10.1109/IGARSS.2015.7326744
N1  - LAMP; 600.079;MILAB
ID  - M. Campos-Taberner2015
ER  -