Dr. Kentaro Kitamura Profile

Dr. Kentaro Kitamura

at Kyushu Institute of Technology

SPIE Involvement:

Author

Publications (1)

Proceedings Article | 19 October 2023 Presentation + Paper

Data downlink prioritization using image classification on-board a 6U CubeSat

Keenan A. Chatar, Ezra Fielding, Kei Sano, Kentaro Kitamura

Proceedings Volume 12729, 127290K (2023) https://doi.org/10.1117/12.2684047

KEYWORDS: Satellites, Education and training, Data modeling, Image classification, Image compression, Stars, Quantization, Electron beam lithography, Data transmission, Imaging systems

Read Abstract +

Nanosatellites are proliferating as low-cost dedicated sensing systems with lean development cycles. Kyushu Institute of Technology (Kyutech) and collaborators have launched a joint venture for a nanosatellite mission, Visible Extragalactic background RadiaTion Exploration by CubeSat (VERTECS). The primary mission is to elucidate the formation history of stars by observing the optical-wavelength cosmic background radiation. The VERTECS satellite will be equipped with a small-aperture telescope and a high-precision attitude control system to capture the astronomical data for analysis on the ground. However, nanosatellites are limited by their onboard memory resources and downlink speed capabilities. Additionally, due to a limited number of ground stations, the satellite mission will face issues meeting the required data budget for mission success. To alleviate this issue, we propose an on-orbit system pipeline to autonomously classify and then compress desirable image data for downlink prioritization and optimization. The system comprises a prototype Camera Controller Board (CCB) which carries a Raspberry Pi Compute Module four which is used for classification and compression. The system uses a lightweight Convolutional Neural Network (CNN) model to classify and determine the desirability of captured image data. The model is designed to be lean and robust to reduce the computational and memory load on the satellite. The model is trained and tested on a novel star field dataset consisting of data captured by the Sloan Digital Sky Survey (SDSS). The dataset is meant to simulate the expected data produced by the 6U satellite. The compression step implements GZip, RICE or HCOMPRESS compression, which are standards for astronomical data. Preliminary testing on the proposed CNN model results in a validation classification accuracy of 100% on the star field dataset, with compression ratios of 3.99, 5.16 and 5.43 for GZip, RICE and HCOMPRESS that were achieved on tested FITS image data.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years