遥感卫星大数据高精度一体化处理技术

doi:10.11959/j.issn.2096-0271.2022013

摘要/Abstract

摘要：

我国空天信息获取技术飞速发展，空天数据呈现出数据量大、种类多、增长快、价值密度相对较低的大数据特点。遥感卫星数据是空天大数据的重要组成部分，如何利用遥感卫星数据的规模效应和异构数据互补优势，提升处理精度并提高处理效率，是遥感卫星大数据处理系统需要解决的关键问题。简要回顾了我国遥感卫星数据地面处理系统的发展历程，指出了地面处理系统面临的核心难点，提出了基于稳定特性挖掘的遥感卫星大数据高精度处理技术的思路，并给出了初步的实现方法，以期为我国空天大数据处理系统的发展提供有益参考。

关键词: 遥感卫星大数据, 地面处理系统, 大数据稳定特性挖掘, 高效处理架构

Abstract:

With the rapid development of China’s aerospace information acquisition technology, aerospace data presents the characteristics of large data volume, large number of types, rapid growth and relatively low value density.Remote sensing satellite data is an important part of aerospace big data.How to make use of the scale effect and the complementary advantages of the data of different satellites, so as to improve the processing accuracy and efficiency, is the key problem to be solved in the remote sensing satellite big data processing system.The development history of our country’s remote sensing satellite ground data processing system was reviewed briefly.The core difficulties faced by the ground processing system were pointed out.A high-precision processing technology for remote sensing satellite big data based on stability feature mining was proposed.And a preliminary implementation was given.The method provides a useful reference for the development of our country’s aerospace big data processing system.

Key words: remote sensing satellite big data, ground processing system, big data stability feature mining, efficient processing architecture

中图分类号:

TP751.1

仇晓兰, 胡玉新, 上官松涛, 付琨. 遥感卫星大数据高精度一体化处理技术[J]. 大数据, 2022, 8(2): 15-27.

Xiaolan QIU, Yuxin HU, Songtao SHANGGUAN, Kun FU. Remote sensing satellite big data high-recision integration processing technology[J]. Big Data Research, 2022, 8(2): 15-27.

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类型	卫星平台	传感器	大气传输	目标及环境
SAR	位置测量误差、速度测量误差、平台姿态测量误差等（含由测量频率不够高导致的无法测试的高频部分的误差）	系统增益测量误差、系统时延误差、通道幅度误差、通道相位误差、天线方向图指向误差、天线方向图形状误差、系统采样量化误差、系统噪声等	对流层延迟误差、大气色散引入误差、法拉第旋转引入误差、大气衰减引入误差等	目标运动、DEM误差、散射去相干、地面干扰等
可见光	位置测量误差、平台姿态测量误差等（含由测量频率不够高导致的无法测试的高频部分的误差）	镜头几何畸变、像元辐射响应不一致、量化误差、暗电流、传感器增益误差、传感器随机噪声、杂散光误差等	大气折射引入误差、大气程辐射引入误差、大气衰减引入误差、大气干扰（云、雾、霾、沙尘）引入误差等	DEM误差、地形（本身地形、临近地形效应）引入误差、临近地物遮挡引入误差等

类型	固定误差	规律性变化误差	随机误差
SAR	安装偏差（平台姿态误差的固定部分、天线指向偏差的固定部分）、系统增益的固定偏差、系统时延的固定偏差、通道幅度和相位的固定部分、天线方向图形状偏差的固定部分等	平台姿态测量误差随温度等的变化部分、系统增益随温度或系统老化时间的变化部分、通道相位随温度等的变化部分、大气模式参数随时间（季节）和空间（极区、温带、热带）的变化部分、目标规律性运动等	卫星位置和速度测量误差、通道幅度和相位的随机部分（相位噪声）、量化噪声、系统噪声、大气引入误差的不确定部分等
可见光	安装偏差（平台姿态测量误差的固定部分）、光谱定标系数（波长、光谱响应）、暗电流、系统调制传递函数的固定部分、绝对辐射校正系数的固定部分、相对辐射校正系数的固定部分等	平台姿态测量误差随温度等的变化部分、传感器增益随温度或系统老化时间的变化部分、通道相位随温度或时间的变化部分、大气模式参数随时间（季节）和空间（极区、温带、热带）的变化部分、光度校正参数等	卫星位置和速度测量误差、量化噪声、传感器随机噪声、大气引入误差的不确定部分等

图像编号	SAR/成像模式	入射角	分辨率/m	升降轨	天线指向
1	GF-3/UFS	49.32°~50.69°	3.0	升轨	左
2	GF-3/UFS	46.59°~48.08°	3.0	升轨	右
3	GF-3/SL	38.93°~39.60°	1.0	升轨	右
4	TerraSAR-X/ST	34.91°~35.45°	0.59	升轨	右

图像组合	传统RD模型/m	不加权统一量纲RD模型/m	加权统一量纲RD模型/m
（1,2）	25.32	24.23	24.36
（1,2,3）	60.52	18.28	18.06
（1,3,4）	109.70	19.99	4.33
（1,2,4）	27.87	18.37	5.49
（1,2,3,4）	95.37	16.14	3.67