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Appl. Sci. 2017, 7(9), 961; doi:10.3390/app7090961

Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery

1
Department of Geoinformatics, University of Seoul, Seoul 02504, Korea
2
Center for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute (KEI), Sejong-si 30147, Korea
*
Author to whom correspondence should be addressed.
Received: 31 July 2017 / Revised: 10 September 2017 / Accepted: 18 September 2017 / Published: 20 September 2017
(This article belongs to the Special Issue Application of Artificial Neural Networks in Geoinformatics)
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Abstract

For ship detection, X-band synthetic aperture radar (SAR) imagery provides very useful data, in that ship targets look much brighter than surrounding sea clutter due to the corner-reflection effect. However, there are many phenomena which bring out false detection in the SAR image, such as noise of background, ghost phenomena, side-lobe effects and so on. Therefore, when ship-detection algorithms are carried out, we should consider these effects and mitigate them to acquire a better result. In this paper, we propose an efficient method to detect ship targets from X-band Kompsat-5 SAR imagery using the artificial neural network (ANN). The method produces the ship-probability map using ANN, and then detects ships from the ship-probability map by using a threshold value. For the purpose of getting an improved ship detection, we strived to produce optimal input layers used for ANN. In order to reduce phenomena related to the false detections, the non-local (NL)-means filter and median filter were utilized. The NL-means filter effectively reduced noise on SAR imagery without smoothing edges of the objects, and the median filter was used to remove ship targets in SAR imagery. Through the filtering approaches, we generated two input layers from a Kompsat-5 SAR image, and created a ship-probability map via ANN from the two input layers. When the threshold value of 0.67 was imposed on the ship-probability map, the result of ship detection from the ship-probability map was a 93.9% recall, 98.7% precision and 6.1% false alarm rate. Therefore, the proposed method was successfully applied to the ship detection from the Kompsat-5 SAR image. View Full-Text
Keywords: synthetic aperture radar (SAR); ship detection; artificial neural network (ANN); Kompsat-5 synthetic aperture radar (SAR); ship detection; artificial neural network (ANN); Kompsat-5
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Hwang, J.-I.; Chae, S.-H.; Kim, D.; Jung, H.-S. Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery. Appl. Sci. 2017, 7, 961.

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