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One-dimensional synthetic aperture radiometer sea surface wind speed retrieval method based on deep learning

A sea surface wind speed and deep learning technology, applied in the field of remote sensing, can solve problems such as difficulties in inverting sea surface wind speed

Active Publication Date: 2021-09-10
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method for inversion of sea surface wind speed based on one-dimensional synthetic aperture radiometer based on deep learning, so as to solve the problem of difficult inversion of sea surface wind speed existing in the prior art

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  • One-dimensional synthetic aperture radiometer sea surface wind speed retrieval method based on deep learning
  • One-dimensional synthetic aperture radiometer sea surface wind speed retrieval method based on deep learning
  • One-dimensional synthetic aperture radiometer sea surface wind speed retrieval method based on deep learning

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Embodiment

[0063] 1. Data set acquisition

[0064] Obtain daily 1°×1° sea level model data from the European Center for Medium-Range Weather Forecasts (ECMWF) from January 1 to December 31, 2016, including sea surface wind speed, sea surface temperature, sea surface wind direction, sea water salinity, cloud liquid water content and atmospheric water vapor content, etc., and screened out data set C containing 80,740 sets of data. C is randomly divided into two parts, the initial training set A and the initial verification set B. The training set and the verification set account for 80% and 20% of the initial data set C, respectively. Input A and B into the radiation transfer forward modeling model and the one-dimensional synthetic aperture microwave radiometer model, output the simulated brightness temperature and put it back into A and B to obtain the training set A' and the verification set B'.

[0065] 2. Construction of deep learning convolutional neural network

[0066] In this ste...

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Abstract

The invention discloses a one-dimensional comprehensive aperture radiometer sea surface wind speed inversion method based on deep learning, comprising the following steps: obtaining sea surface temperature, seawater salinity, relative sea surface wind direction, incident angle, atmospheric water vapor content and cloud liquid water content; Input the sea surface temperature, seawater salinity, sea surface relative wind direction, incident angle, atmospheric water vapor content and cloud liquid water content into the radiation transfer forward modeling model to obtain simulated brightness temperature; The wind direction, incident angle, atmospheric water vapor content, cloud liquid water content and simulated brightness temperature are input to the deep learning inversion model based on the convolutional neural network to obtain the sea surface wind speed. The inversion accuracy is improved, and a method is provided for inversion of sea surface wind speed by one-dimensional synthetic aperture microwave radiometer.

Description

technical field [0001] The invention relates to the technical field of remote sensing, in particular to a method for inversion of sea surface wind speed by a one-dimensional synthetic aperture radiometer based on deep learning. Background technique [0002] Sea surface wind speed affects the air-sea interaction and is an important physical quantity for marine environment detection. Microwave remote sensors have all-day and all-weather observation capabilities and certain subsurface detection capabilities, and are one of the main means of detecting sea surface wind speed. But for spaceborne platforms, the size and weight of the antenna are strictly limited, and the spatial resolution is low. The synthetic aperture microwave radiometer is the product of the application of interferometric technology to earth observation. It uses a small aperture antenna array to solve the contradiction between the high resolution of the microwave radiometer and the huge antenna, and significan...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S13/90G01S7/295
CPCG01S7/2955G01S13/9027
Inventor 艾未华乔俊淇刘茂宏郭朝刚
Owner NAT UNIV OF DEFENSE TECH
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