Name
Abstract | ||
|---|---|---|
In this study, we propose a method to detect dry docks, a harbour man-made object which is hard to recognize, using representation learning in satellite images. Dry docks are coastal structures which may include ships for repairing purposes, and they exist in harbour regions. The search space is pruned by making use of two low-level features that invariantly define docks, and remaining samples are used to train a representation learning system. Experimental results suggest that classification methods using learned features have similar performances to those using handcrafted features, which are proposed by the field expert. The results also provide insight on the applicability of the same methodology on detection of different objects in remotely sensed images, without wasting any effort. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SIU.2013.6531554 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
representation learning,object recognition | Computer vision,Object detection,Satellite,Pattern recognition,Computer science,Artificial intelligence,Contextual image classification,Feature learning,Dry dock | Conference |
ISSN | Citations | PageRank |
2165-0608 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umit Rusen Aktas | 1 | 0 | 0.34 |
| Orhan Firat | 2 | 281 | 29.13 |
| Yarman Vural, F.T. | 3 | 73 | 6.17 |