Open AccessArticle
Onboard Image Processing System for Hyperspectral Sensor
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Hiroki Hihara 1,2,*, Kotaro Moritani 1, Masao Inoue 1, Yoshihiro Hoshi 1, Akira Iwasaki 2, Jun Takada 3, Hitomi Inada 4, Makoto Suzuki 5, Taeko Seki 6, Satoshi Ichikawa 6 and Jun Tanii 7
1
NEC Space Technologies, Ltd., 1-10, Nisshin-cho, Fuchu, Tokyo 183-8551, Japan
2
Research Center for Advanced Science and Technology, the University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
3
Central Research Laboratory, NEC Corporation, 1753, Shimonumabe, Nakahara-Ku, Kawasaki, Kanagawa 211-8666, Japan
4
Space Systems Division, NEC Corporation, 1-10, Nisshin-cho, Fuchu, Tokyo 183-8551, Japan
5
Institute of Space Astronautical Science (ISAS), Japan Aerospace Exploration Agency (JAXA), 3-1-1 Yoshinodai, Chuo-ku, Sagamihara, Kanagawa 252-5210, Japan
6
Aerospace Research and Development Directorate, Japan Aerospace Exploration Agency (JAXA), 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505, Japan
7
Japan Space Systems, 3-5-8 Shibakoen, Minato-ku, Tokyo 105-0011, Japan
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Abstract
Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast
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Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost.
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