Precise Ship Location With CNN Filter Selection From Optical Aerial Images
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http://hdl.handle.net/10045/95047
Título: | Precise Ship Location With CNN Filter Selection From Optical Aerial Images |
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Autor/es: | Alashhab, Samer | Gallego, Antonio-Javier | Pertusa, Antonio | Gil, Pablo |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial | Automática, Robótica y Visión Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Artificial neural networks | Learning systems | Object detection | Remote sensing |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos | Ingeniería de Sistemas y Automática |
Fecha de publicación: | 16-jul-2019 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Access. 2019, 7: 96567-96582. doi:10.1109/ACCESS.2019.2929080 |
Resumen: | This paper presents a method that can be used for the efficient detection of small maritime objects. The proposed method employs aerial images in the visible spectrum as inputs to train a categorical convolutional neural network for the classification of ships. A subset of those filters that make the greatest contribution to the classification of the target class is selected from the inner layers of the CNN. The gradients with respect to the input image are then calculated on these filters, which are subsequently normalized and combined. Thresholding and a morphological operation are then applied in order to eventually obtain the localization. One of the advantages of the proposed approach with regard to previous object detection methods is that it is only required to label a few images with bounding boxes of the targets to be trained for localization. The method was evaluated with an extended version of the MASATI (MAritime SATellite Imagery) dataset. This new dataset has more than 7 000 images, 4 157 of which contain ships. Using only 14 training images, the proposed approach achieves better results for small targets than other well-known object detection methods, which also require many more training images. |
Patrocinador/es: | 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 the Babcock MCS Spain under Project INAER4-14Y (IDI-20141234). |
URI: | http://hdl.handle.net/10045/95047 |
ISSN: | 2169-3536 |
DOI: | 10.1109/ACCESS.2019.2929080 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1109/ACCESS.2019.2929080 |
Aparece en las colecciones: | INV - AUROVA - Artículos de Revistas INV - GRFIA - Artículos de Revistas |
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Archivo | Descripción | Tamaño | Formato | |
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2019_Alashhab_etal_IEEEAccess.pdf | 24,9 MB | Adobe PDF | Abrir Vista previa | |
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