研究目的
Investigating the implementation of a compressive sensing method on a low power consumption GPU for hyperspectral image compression to achieve real-time performance with limited power requirements.
研究成果
The study demonstrates that using a low power consumption GPU like the Jetson TX1 for implementing compressive sensing techniques can achieve real-time performance in compressing hyperspectral images with very limited power requirements. The use of integer data types does not significantly affect the accuracy of reconstruction while reducing processing time. However, further research is needed for deployment in space due to radiation tolerance requirements.
研究不足
The Jetson TX1 board is not yet radiation-tolerant, which prevents its use on satellites. Future research is needed to compare the proposed methodology with other traditional compression schemes in terms of accuracy and computational performance, and to measure power consumption for different hardware resources.
1：Experimental Design and Method Selection:
The study implements the Hyperspectral Coded Aperture (HYCA) method on a Jetson TX1 board, a low power consumption GPU, to perform random projections for hyperspectral image compression.
2：Sample Selection and Data Sources:
A synthetic dataset was generated from spectral signatures randomly selected from the United States Geological Survey (USGS), simulating natural spatial patterns.
3：List of Experimental Equipment and Materials:
The Jetson TX1 board, incorporating a Nvidia MaxwellTM GPU with 256 NVIDIA CUDA cores, a quad-core ARM cortex-A57 MPCore processor, 4GB LPDDR4 memory, and 16GB eMMC
4：1 flash storage. Experimental Procedures and Operational Workflow:
The compression process was performed on the Jetson TX1 board using different data types (float64, float32, int16) to evaluate performance and accuracy.
5：Data Analysis Methods:
The accuracy of the compression was evaluated by reconstructing the image from the compressed measurements and comparing it to the original using Peak Signal to Noise Ratio (PSNR).
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