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Abstract
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.
Highlights
Owing to the characteristics of wide coverage, long duration, remote access, and high data collection volume, earth observation satellites play a key role in urban planning, traffic surveillance, geological exploration, disaster assessment, military reconnaissance, etc
To solve the problems above, this paper presents an on-board ship detection scheme based on Commercial Off-The-Shelf (COTS) component and deep learning, which can be used to achieve near real-time on-board processing by micro-nano satellite computing platform
In order to verify the effectiveness of the scheme in this paper, we established a dataset collected from Google Earth for the lack of publicly available datasets for on-board ship detection
Summary
Owing to the characteristics of wide coverage, long duration, remote access, and high data collection volume, earth observation satellites play a key role in urban planning, traffic surveillance, geological exploration, disaster assessment, military reconnaissance, etc Among these satellites, micro-nano satellites have the specific advantages of small size, low power consumption, short development cycle, suitable for networking and constellation, and low cost to complete many complex space missions. Compared with the traditional ground processing flow of ship detection, the advantages of on-board processing flow of ship detection are as follows: (1) the gigabyte level images are simplified to megabyte level target patches and information by on-board ship detection It can reduce the compression, transmission and storage pressure for emergencies. It can reduce the compression, transmission and storage pressure for emergencies. (2) It simplifies the process of ground equipment and directly delivers the concerned information to users from the satellite. (3) Users can acquire information in near real-time while traditional processing flow needs several hours or days
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