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15th workshop gRFIA

9th Music Encoding Conference (MEI 2021)


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  1. Nieto-Hidalgo, M.; Gallego, A.J.; Gil, P.; Pertusa, A.
    "Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images"
    IEEE Transactions on Geoscience and Remote Sensing, vol. 56, pp. 5217-5230 (2018)
    : bibtex : URL

    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.

@article {
 author = "Nieto-Hidalgo, M.; Gallego, A.J.; Gil, P.; Pertusa, A.",
 title  = "Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images",
 issn = "0196-2892",
 journal = "IEEE Transactions on Geoscience and Remote Sensing",
 month = "September",
 number = "9",
 pages = "5217-5230",
 volume = "56",
 year = "2018"
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