A deeply-enforced method for extracting ships in remote sensing satellite video data

Likun Liu

doi:10.1117/12.2550278

12 March 2020 A deeply-enforced method for extracting ships in remote sensing satellite video data

Likun Liu

Proceedings Volume 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 1143810 (2020) https://doi.org/10.1117/12.2550278
Event: 2019 International Conference on Optical Instruments and Technology, 2019, Beijing, China

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.

Citation Download Citation

Likun Liu "A deeply-enforced method for extracting ships in remote sensing satellite video data", Proc. SPIE 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 1143810 (12 March 2020); https://doi.org/10.1117/12.2550278

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KEYWORDS

Image segmentation

Remote sensing

Satellites

Detection and tracking algorithms

Video

Feature extraction

Image processing

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