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Proceedings Paper

A deeply-enforced method for extracting ships in remote sensing satellite video data
Author(s): Likun Liu
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Paper Abstract

Traditional approaches for remote sensing image segmentation are mature in certain respect, as the new spaceborne technology of continuous observation satellite video emerges, it arises a new demand for moving object detection from this new data source. In the field of computer vision, deep learning technique has achieved outstanding performance for general images. In the research, a deep learning based method is introduced and several modifications are made in the processing steps. Faster Convolutional Neural Network (Faster CNN) algorithm is selected as the basal pipeline and conditional random field is used to generate finer detail proposals. After enforced iterations, the computed extraction result of ships in remote sensing satellite video data is compared with original Faster CNN method which demonstrates an improved target detection output in different tests.

Paper Details

Date Published: 12 March 2020
PDF: 11 pages
Proc. SPIE 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 1143810 (12 March 2020); doi: 10.1117/12.2550278
Show Author Affiliations
Likun Liu, China Ship Development and Design Ctr. (China)


Published in SPIE Proceedings Vol. 11438:
2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology
Guohai Situ; Xun Cao; Wolfgang Osten, Editor(s)

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