Dry Dock Detection in Satellite Images with Representation Learning

Date

2013-04-26

Author

Aktaş, Ümit Ruşen
Firat, Orhan
Yarman Vural, Fatoş Tunay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Object recognition, Representation learning

URI

https://hdl.handle.net/11511/53601

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

Ü. R. Aktaş, O. Firat, and F. T. Yarman Vural, “Dry Dock Detection in Satellite Images with Representation Learning,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53601.