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A new method for high precision processing of multi-system Earth observation satellite data

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Geo-information Processing and Application System (GIPAS), Chinese Academy of Sciences, Beijing 100190, China

    Key Laboratory of Network Information System Technology (NIST), Chinese Academy of Sciences, Beijing 100190, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：fukun@aircas.ac.cn
  • Introduction：付琨，研究方向为空天遥感数据处理与应用。E-mail：fukun@aircas.ac.cn
FU Kun1234,  
  • Affiliation：

    Key Laboratory of Geo-information Processing and Application System (GIPAS), Chinese Academy of Sciences, Beijing 100190, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

QIU Xiaolan13,  
  • Affiliation：

    Key Laboratory of Geo-information Processing and Application System (GIPAS), Chinese Academy of Sciences, Beijing 100190, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

HAN Bing13,  
  • Affiliation：

    Key Laboratory of Network Information System Technology (NIST), Chinese Academy of Sciences, Beijing 100190, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

SUN Xian23

Resümee

Earth observation satellites, such as optical and SAR satellites, require processing such as imaging, radiometric/geometric correction, and continuous accumulation in order to provide high-precision, stable, and time continuous data and features for computer interpretation. Traditional medium and low resolution Earth observation satellites typically perform pixel-by-pixel processing based on the assumption of ideal point targets, which means that the ground object grid has a invariant time-frequency characteristic. However, the working modes of advanced satellite systems, such as high-resolution, wide-swath, large squint angle, and multi-channel, are more complex, and their data processing is very sensitive to the errors generated in the whole chain of the satellite to ground, which puts higher requirements on the accuracy of imaging parameter calibration or estimation. Hence, the method of assuming sensor pixels as ideal point targets for parameter estimation, imaging, and correction processing is no longer able to meet the processing accuracy requirements. Moreover, in recent years, the new development of multi-system satellite network collaboration and fusion applications has made it difficult to characterize and model the features of multi-source and multi-temporal data based on the current ideal point target assumption. To this end, this article proposes a new method for high-precision processing of multi-system remote sensing satellite imaging data. Firstly, the concept and characterization theory of “Hyper-pixel” are proposed, and an accurate imaging model based on hyper-pixels is established. Then, by mining stable features of hyper-pixels, and inspired by generative adversarial learning mechanisms, high-precision estimation and continuous refinement of high coupling imaging parameters are achieved. This effectively improves the accuracy of multi-system remote sensing satellite imaging data products, and provides better data input for computer interpretation.

Schlüsselwort

remote sensing satellite;hyper-pixel;imaging processing;radiometric correction;geometric correction;deep learning

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