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Deep Learning Attitude Sensor provides real-time image recognition from satellite orbit

(18 January 2019 - Tokyo Tech) Researchers at Tokyo Tech have developed a low-cost star tracker and Earth sensor made from commercially available components.

The star tracker is designed for use with micro-satellites in handling calibration observations, operation verification tests, and long-term performance monitoring during orbit. Embodying the concept of edge computing, the Earth camera performs image recognition while in orbit using a simple AI that identifies land use and vegetation distribution. Utilizing the acquired topography data, assessments can also be conducted using a novel 3-axis attitude estimation method. The star tracker and Earth sensor are installed on the Japan Aerospace Exploration Agency's (JAXA) Epsilon-4 rocket, scheduled for launch on January 17, 2019 from the Uchinoura Space Center in Kagoshima Prefecture, Japan.

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Deep Learning Attitude Sensor control box (left), camera unit (middle) and mother boards (right) (courtesy: Tokyo Tech)

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Camera unit undergoing vibration testing (courtesy: Tokyo Tech)

Research Goals

To perform functions such as communicating with ground stations and directing solar cell paddles toward the sun for power and temperature control, satellites use attitude sensors to determine their orientation (attitude). The Tokyo Tech research group led by Assistant Professor Yoichi Yatsu has developed a star tracker and an Earth sensor that uses deep learning to determine attitude in space. With no ground to distinguish directionality, the device constantly tracks multiple fixed stars to achieve high accuracy, while the Earth sensor performs attitude estimation based on images of the Earth.

This Deep Learning Attitude Sensor (DLAS) was developed with three goals in mind. The first is to demonstrate that a low-cost star tracker made from inexpensive, high-performance commercially available components can effectively operate in space. The plan is to capture images of stars in orbit under various conditions to calibrate the sensor system and determine attitude based on novel algorithms, and demonstrate long-term operation with a test period of one year .

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Attitude determination using a star tracker (left) and Earth sensor (right) (courtesy: Tokyo Tech)

The second goal is to conduct orbital testing of real-time image recognition using deep learning. The Earth is photographed using two compact visible light cameras incorporated in the baffle of the star tracker. The 8-megapixel images taken are processed in about 4 seconds using a specially developed high-speed, lightweight image identification algorithm. Recognition of vegetation and land use is performed over nine categories, including green terrain, deserts, oceans, clouds, and outer space. This will be the first demonstration of real-time image recognition in space using deep learning. In orbit, more than 1,000 images are taken as learning data and transferred to the ground for use in satellite image application tests. The third goal is the application of this image identification technology, and the evaluation of the technologies for estimating 3-axis attitude using land features obscured by clouds and comparing it with map data prerecorded in the onboard computer.

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Example of vegetation/land-use identification using an Earth image from the ISS (courtesy: Tokyo Tech)

Hardware development for DLAS completed in April 2018 and was incorporated into the Innovative Satellite Technology Demonstration-1 (RAPIS-1) developed by JAXA. After about six months of system environment testing and operation rehearsals, it will be launched from the JAXA Uchinoura Space Center in Kagoshima Prefecture using Epsilon-4 on January 17, 2019, and will enter into Sun-synchronous orbit at an altitude of 500 km. DLAS operation is scheduled to start after completion of the RAPIS-1 checkout, and after confirming initial operation, each sensor system will undergo calibration for about one month. Afterwards, it will enter into mission operation for one year.

JAXA entrusted development of the RAPIS-1 satellite / control system and satellite operation to Axelspace Corporation, which is a startup company established by people involved in the development of nanosatellites at the University of Tokyo and Tokyo Tech. This marks a major turning point in Japanese space development, which until now had been led by major electronics manufacturers, and will be a memorable flight operation for those related to private space development, which was "palm size" at universities fifteen years ago.

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The Innovative Satellite Technology Demonstration-1 (RAPIS-1) (courtesy: JAXA)