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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Onboard Image Processing System for Hyperspectral Sensor.

Hiroki Hihara^1,2, Kotaro Moritani³, Masao Inoue⁴

¹NEC Space Technologies, Ltd., 1-10, Nisshin-cho, Fuchu, Tokyo 183-8551, Japan. h-hihara@bc.jp.nec.com.

Sensors (Basel, Switzerland)

|September 26, 2015

Summary

This summary is machine-generated.

A new lossless image compression algorithm, Fast, Efficient, Lossless Image compression System (FELICS), enhances onboard processing for hyperspectral sensors. This improves data transmission efficiency and reduces satellite system size and weight.

Keywords:

Golomb-Rice coding hierarchical prediction hyperspectral sensor lossless image compression onboard correction predictive coding resolution scaling smile correction

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Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

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Published on: April 14, 2020

Area of Science:

Earth observation
Satellite technology
Image processing

Background:

Hyperspectral sensors on Earth observation satellites generate large data volumes requiring efficient transmission.
Existing systems face challenges with data downlink capacity due to high channel counts (>100).
Reducing sensor system size, weight, and power consumption is critical for space missions.

Purpose of the Study:

To develop an onboard image processing system for hyperspectral sensors.
To maximize image data transmission efficiency for large volume and high-speed data downlink.
To implement a fast, small-footprint lossless image compression algorithm.

Main Methods:

Developed a fast lossless image compression algorithm based on Fast, Efficient, Lossless Image compression System (FELICS).
Applied a two-dimensional interpolation prediction and adaptive Golomb-Rice coding to enhance FELICS.
Integrated the algorithm into onboard correction circuitry for Complementary Metal Oxide Semiconductor (CMOS) sensors.

Main Results:

Achieved superior performance in speed and complexity with the enhanced FELICS algorithm.
The system supports progressive decompression using resolution scaling.
Demonstrated increased coding efficiency and compression speed, enlarging effective signal transmission capacity.

Conclusions:

The developed onboard image compression circuitry reduces onboard hardware requirements by multiplexing sensor signals.
The system is embedded into the data formatter without increasing size, weight, power consumption, or fabrication cost.
This advancement enhances hyperspectral sensor system efficiency for Earth observation satellites.