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Proceedings Paper

Ship detection leveraging deep neural networks in WorldView-2 images
Author(s): T. Yamamoto; Y. Kazama
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Paper Abstract

Interpretation of high-resolution satellite images has been so difficult that skilled interpreters must have checked the satellite images manually because of the following issues. One is the requirement of the high detection accuracy rate. The other is the variety of the target, taking ships for example, there are many kinds of ships, such as boat, cruise ship, cargo ship, aircraft carrier, and so on. Furthermore, there are similar appearance objects throughout the image; therefore, it is often difficult even for the skilled interpreters to distinguish what object the pixels really compose. In this paper, we explore the feasibility of object extraction leveraging deep learning with high-resolution satellite images, especially focusing on ship detection. We calculated the detection accuracy using the WorldView-2 images. First, we collected the training images labelled as “ship” and “not ship”. After preparing the training data, we defined the deep neural network model to judge whether ships are existing or not, and trained them with about 50,000 training images for each label. Subsequently, we scanned the evaluation image with different resolution windows and extracted the “ship” images. Experimental result shows the effectiveness of the deep learning based object detection.

Paper Details

Date Published: 4 October 2017
PDF: 9 pages
Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 1042711 (4 October 2017); doi: 10.1117/12.2277533
Show Author Affiliations
T. Yamamoto, Hitachi, Ltd. (Japan)
Y. Kazama, Hitachi Asia, Ltd. (Singapore)


Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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