Next Article in Journal
Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery
Next Article in Special Issue
UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
Previous Article in Journal
Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection
Previous Article in Special Issue
Impact of the Elevation Angle on CYGNSS GNSS-R Bistatic Reflectivity as a Function of Effective Surface Roughness over Land Surfaces
Article

On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component

by 1,2,3, 1,2,3, 1,2,3,* and 4
1
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
2
Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 100191, China
3
Beijing Key Laboratory of Digital Media, Beijing 100191, China
4
DFH Satellite Co., Ltd., Beijing 100094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 762; https://doi.org/10.3390/rs11070762
Received: 1 March 2019 / Revised: 25 March 2019 / Accepted: 26 March 2019 / Published: 29 March 2019
(This article belongs to the Special Issue Applications of Micro- and Nano-Satellites for Earth Observation)
Micro-nano satellites have provided a large amount of remote sensing images for many earth observation applications. However, the hysteresis of satellite-ground mutual communication of massive remote sensing images and the low efficiency of traditional information processing flow have become the bottlenecks for the further development of micro-nano satellites. To solve this problem, this paper proposes an on-board ship detection scheme based on deep learning and Commercial Off-The-Shelf (COTS) component, which can be used to achieve near real-time on-board processing by micro-nano satellite computing platform. The on-board ship detection algorithm based on deep learning consists of a feature extraction network, Region Proposal Network (RPN) with square anchors, Global Average Pooling (GAP), and Bigger-Left Non-Maximum Suppression (BL-NMS). With the help of high performance COTS components, the proposed scheme can extract target patches and valuable information from remote sensing images quickly and accurately. A ground demonstration and verification system is built to verify the feasibility and effectiveness of our scheme. Our method achieves the performance with 95.9% recall and 80.5% precision in our dataset. Experimental results show that the scheme has a good application prospect in micro-nano satellites with limited power and computing resources. View Full-Text
Keywords: on-board processing; ship detection; micro-nano satellite; deep learning; COTS component on-board processing; ship detection; micro-nano satellite; deep learning; COTS component
Show Figures

Graphical abstract

MDPI and ACS Style

Yao, Y.; Jiang, Z.; Zhang, H.; Zhou, Y. On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component. Remote Sens. 2019, 11, 762. https://doi.org/10.3390/rs11070762

AMA Style

Yao Y, Jiang Z, Zhang H, Zhou Y. On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component. Remote Sensing. 2019; 11(7):762. https://doi.org/10.3390/rs11070762

Chicago/Turabian Style

Yao, Yuan, Zhiguo Jiang, Haopeng Zhang, and Yu Zhou. 2019. "On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component" Remote Sensing 11, no. 7: 762. https://doi.org/10.3390/rs11070762

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop