PT Unknown
AU M. Campos-Taberner
   Adriana Romero
   Carlo Gatta
   Gustavo Camps-Valls
TI Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination
BT IEEE International Geoscience and Remote Sensing Symposium IGARSS2015
PY 2015
BP 4169
EP 4172
DI 10.1109/IGARSS.2015.7326744
AB 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.
ER