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On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery.

Baogui Qi¹, Hao Shi^2,3, Yin Zhuang⁴

¹Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China. qibaogui@bit.edu.cn.

Sensors (Basel, Switzerland)

|April 26, 2018

Summary

This summary is machine-generated.

This study introduces a novel on-board, real-time optical remote-sensing image processing architecture. The developed system, utilizing field-programmable gate array (FPGA) and digital signal processor (DSP) co-processing, significantly reduces delays for time-sensitive applications.

Keywords:

geometric correction preprocessing real-time relative radiation correction remote sensing

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Area of Science:

Remote Sensing Technology
Optical Image Processing
Geospatial Data Analysis

Background:

Optical remote-sensing imagery is crucial for geological exploration and disaster prevention.
Traditional ground-based preprocessing of remote-sensing data introduces significant delays, hindering real-time applications.
On-board, real-time image preprocessing is essential to overcome these limitations.

Purpose of the Study:

To propose a real-time processing architecture for on-board imagery preprocessing.
To address the computational burden and delay issues in traditional remote-sensing data workflows.
To enable efficient, on-the-fly preprocessing of optical remote-sensing data.

Main Methods:

Developed a hierarchical optimization and mapping method for hardware implementation of preprocessing algorithms.
Designed a co-processing system integrating a field-programmable gate array (FPGA) and a digital signal processor (DSP).
Optimized the FPGA-DSP system for real-time preprocessing capabilities.

Main Results:

The proposed hierarchical optimization effectively reduces the computation burden for on-board processing.
The FPGA-DSP co-processing system achieves real-time performance for image preprocessing.
Experimental results validate the system's potential for resource- and power-constrained on-board processors.

Conclusions:

The developed architecture offers a viable solution for on-board, real-time remote-sensing image preprocessing.
This approach significantly mitigates delays associated with traditional ground-based processing.
The system demonstrates practical applicability in scenarios demanding immediate geospatial data analysis.