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Deviation influence factor analysis method for satellite radiation data

A technology of influencing factors and analysis methods, which is applied in the field of satellite radiation data analysis, can solve problems such as unsuitable high-dimensional data and high time complexity, and achieve the effect of improving the technical level, improving accuracy and stability, and improving speed and accuracy

Pending Publication Date: 2021-06-08
NORTH CHINA UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

The Wrapper method has a good classification accuracy, but the time complexity is high and it is not suitable for high-dimensional data

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  • Deviation influence factor analysis method for satellite radiation data
  • Deviation influence factor analysis method for satellite radiation data
  • Deviation influence factor analysis method for satellite radiation data

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[0031] The technical solution of this patent will be further described in detail below in conjunction with specific embodiments.

[0032] A method for analyzing the deviation influencing factors of satellite radiation data. Aiming at the problems existing in the traditional feature selection method and the advantages of the XGBoost method, the importance of the features of the XGBoost algorithm is calculated and sorted, and then the key influencing factors of the satellite radiation data deviation are screened out.

[0033] 1. Data set selection

[0034] The Moderate Resolution Imaging Spectroradiometer (MODIS) is carried on the Terra and Aqua satellites and is a very important sensor. MODIS is currently recognized as an earth observation instrument with stable performance and good calibration at home and abroad, and has a complex on-board calibration analysis system. With the continuous development of satellite remote sensing technology in my country, remote sensing data has ...

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Abstract

The invention discloses a deviation influence factor analysis method for satellite radiation data, and belongs to the technical field of satellite radiation data analysis. The method comprises three steps of data set selection, data preprocessing and deviation influence factor analysis and calculation. When themethod faces a large amount of multi-dimensional data, the correlation between the deviation of the satellite radiation data and the input characteristics is analyzed, and influence factors of the deviation are preliminarily screened out; and then feature importance is calculated and sorted by adopting a machine learning method, and finally a key influence factor of the deviation is screened out. According to the method, the feature importance value is calculated by using a feature fusion mechanism, so that compared with similar algorithms, the method has the advantages that more time is saved, the speed and precision can be improved, and a foundation is laid for improving the technical level of in-orbit radiation calibration and inspection of the reflection band in China and further improving the in-orbit calibration precision and stability.

Description

technical field [0001] The invention belongs to the technical field of satellite radiation data analysis, in particular to a method for analyzing satellite radiation data deviation influencing factors. Background technique [0002] According to the relevant literature, there are many methods of feature selection, among which the common feature selection methods can be divided into filtering (Filter) and encapsulation (Wrapper). The Filter method has high selection efficiency, but is sensitive to noise data, and is generally used for preliminary screening of features. The Wrapper method has a good classification accuracy, but it has high time complexity and is not suitable for high-dimensional data. [0003] The screening of key influencing factors is very important for fast radiative transfer simulation, and it is also the key to the prediction effect of the model. The XGBoost algorithm is a boosting tree-based machine learning system proposed by Chen et al. based on a lar...

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Application Information

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IPC IPC(8): G06K9/62G06N20/20
CPCG06N20/20G06F18/214Y02A90/10
Inventor 曹丹阳陈明珠宋歆睿马艳红
Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY
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