SAR wind field sea wave joint inversion method and system based on data driving

Abstract

The invention belongs to the field of wind field sea wave inversion, and provides an SAR wind field sea wave joint inversion method and system based on data driving. The method comprises the following steps: collecting SAR data, and obtaining a plurality of small images in the SAR data; extracting a backscattering coefficient, a normalized variance, an incident angle and a frequency domain characteristic quantity of the small image, and constructing an input data set; obtaining ECMWF data, performing space-time matching on a wind speed, an effective wave height and an average wave period for each pixel of the small image, and constructing an output data set based on the matched wind speed, effective wave height and average wave period; inputting the input data set and the output data set into a convolutional neural network model for training to obtain a trained convolutional neural network model; and inputting the SAR data of the sea area to be measured into the trained convolutional neural network model to obtain the wind speed, the significant wave height and the average wave period of the sea area to be measured.

Description

technical field [0001] The invention belongs to the technical field of wind field sea wave inversion, and in particular relates to a data-driven SAR wind field sea wave joint inversion method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The sea surface wind field and waves are important ocean dynamic process phenomena, which play an important role in the exchange of matter and energy between the upper ocean process and the air-sea interface, and are the ocean phenomena most directly related to human activities. It is of great significance to comprehensively and systematically obtain the observation information of sea surface wind field and wave and master its laws for marine scientific research, disaster prevention and mitigation, and national defense construction. [0004] The current sea surface wind field and wave inform...

AI Technical Summary

Problems solved by technology

This situation leads to the following problems: First, obtaining the initial guess spectrum from external data sources will introduce more uncertain factors that affect the inversion results; second, the process of obtaining the initial guess spectrum requires additional time, which will reduce the The timeliness of inversion; finally, SAR cannot be used as an independent observation source to realize the detection of ocean waves

[0009] In summary, most of the current traditional methods for SAR wind field and wave inversion rely on wind direction and initial guess spectrum information (wind direction is the initial information that needs to be input when inverting wind speed, and initial guess spectrum is the input that needs to be input when inverting ocean wave spectrum Initial spectrum information), most of the wind direction and initial guess spectrum information need to be obtained from external data sources, which makes SAR unable to independently realize the detection of wind field and sea waves

It takes extra time to obtain data from external data sources, and the accuracy of external data will be affected by many factors, which will reduce the timeliness of SAR wind field and wave inversion, and cause the inversion accuracy of wind field and wave parameters to be affected by more factors

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