Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images

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Title: Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images
Authors: Nieto-Hidalgo, Mario | Gallego, Antonio-Javier | Gil, Pablo | Pertusa, Antonio
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial | Informática Industrial y Redes de Computadores | Automática, Robótica y Visión Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal
Keywords: Neural networks | Oil spill detection | Radar detection | Side-looking airborne radar (SLAR) | Supervised learning
Knowledge Area: Lenguajes y Sistemas Informáticos | Ingeniería de Sistemas y Automática
Issue Date: Sep-2018
Publisher: IEEE
Citation: IEEE Transactions on Geoscience and Remote Sensing. 2018, 56(9): 5217-5230. doi:10.1109/TGRS.2018.2812619
Abstract: This paper presents a system for the detection of ships and oil spills using side-looking airborne radar (SLAR) images. The proposed method employs a two-stage architecture composed of three pairs of convolutional neural networks (CNNs). Each pair of networks is trained to recognize a single class (ship, oil spill, and coast) by following two steps: a first network performs a coarse detection, and then, a second specialized CNN obtains the precise localization of the pixels belonging to each class. After classification, a postprocessing stage is performed by applying a morphological opening filter in order to eliminate small look-alikes, and removing those oil spills and ships that are surrounded by a minimum amount of coast. Data augmentation is performed to increase the number of samples, owing to the difficulty involved in obtaining a sufficient number of correctly labeled SLAR images. The proposed method is evaluated and compared to a single multiclass CNN architecture and to previous state-of-the-art methods using accuracy, precision, recall, F-measure, and intersection over union. The results show that the proposed method is efficient and competitive, and outperforms the approaches previously used for this task.
Sponsor: This work was supported in part by the Spanish Government’s Ministry of Economy, Industry, and Competitiveness under Project RTC-2014-1863-8 and in part by Babcock MCS Spain under Project INAER4-14Y (IDI-20141234).
URI: http://hdl.handle.net/10045/78747
ISSN: 0196-2892 (Print) | 1558-0644 (Online)
DOI: 10.1109/TGRS.2018.2812619
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 IEEE
Peer Review: si
Publisher version: https://doi.org/10.1109/TGRS.2018.2812619
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