Dry Dock Detection in Satellite Images with Representation Learning

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


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In this paper, a novel automatic geo-spatial object recognition algorithm from high resolution satellite imagery is proposed. The proposed algorithm consists of two main steps; the generation of hypothesis with a local feature based algorithm and verification step with a shape based approach. The superiority of this method is the ability of minimization of false alarm number in the recognition and this is because object shape includes more characteristic and discriminative information about object identity ...
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Lineament extraction and analysis is one of the routines in mapping large areas using remotely-sensed data, most of which is the satellite images. In this study, we aimed to test different lineament extraction techniques including single band, multiband enhancements and spatial domain filtering techniques. A fast algorithm has been developed for time and cost limited surveys in an area with known dominant and/or any selected orientation of lineaments. During the study for single band analysis, histogram equ...
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.