Effigis Geo Solutions Inc.
Dahmane M.,Computer Research Institute of Montreal |
Foucher S.,Computer Research Institute of Montreal |
Beaulieu M.,Computer Research Institute of Montreal |
Bouroubi Y.,Effigis Geo Solutions inc. |
Benoit M.,Effigis Geo Solutions inc.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017
Various generative and discriminative methods have been transferred from the computer vision field to remote sensing applications using different low and high semantic level descriptors. However, as classical approaches have shown their limits in representation learning and are not intended to deal with the great variability of the data. With the emergence of large-scale annotated datasets in vision, the convolutional deep approaches represent the most winning solutions by supporting this variability with spatial context integration through different semantic abstraction levels. In the lack of annotated remote sensing data, in this paper, we are comparing the performances of deep features produced by six different CNNs that have been trained on well established computer vision datasets with respect to the detection of small objects (cars) in very high resolution Pleiades imagery. Our findings show good generalization performance and are very encouraging for future applications. © Springer International Publishing AG 2017.
Effigis Geo Solutions Inc. | Date: 2012-01-27
Earth observation, satellite positioning and telecommunication technologies dedicated to improve business processes. Consulting and software engineering services in the fields of earth observation, satellite positioning and telecommunication technologies dedicated to improve business processes.