Corn growth parameter active and passive remote sensing inversion method based on data augmentation and deep learning

Abstract

The invention provides a corn growth parameter active and passive remote sensing inversion method based on data augmentation and deep learning, and the method comprises the steps: carrying out data preprocessing: respectively preprocessing an optical image and a radar image, obtaining input data, and normalizing the input data; a Beta data mixing method is used for simultaneously carrying out mixing processing on limited actually-measured leaf area indexes, biomass data and corresponding remote sensing data so as to establish a large number of virtual training samples to carry out data augmentation; a leaf area index and biomass inversion model is constructed by combining a twin deep network based on a gating mechanism with optical and radar remote sensing data. The number of field actualmeasurement samples is increased by providing data augmentation, and a twin neural network model of a gating mechanism is constructed, so that a deep learning method with better nonlinear fitting capability is applied to active and passive inversion of corn LAI and biomass, and the inversion precision is improved.

Description

technical field [0001] The invention relates to the field of remote sensing inversion, in particular to a method for active and passive remote sensing inversion of corn growth parameters based on data augmentation and deep learning. Background technique [0002] Leaf area index (LAI) and biomass are important corn growth parameters, which can provide important information for corn growth status assessment, temperature stress, water stress, pest control, early yield estimation, etc., and are currently widely used in field management decisions and early yield estimation . The traditional method of obtaining LAI and biomass mainly relies on field sampling and manual measurement, which is time-consuming and labor-intensive. The remote sensing inversion method relies on a small amount of measured data and remote sensing data to construct an inversion model, which can realize large-scale measurement. Currently, remote sensing inversion methods include inversion methods based on ...

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Problems solved by technology

[0005] The present invention aims at the problem that the inversion accuracy is not high in the inversion research of maize LAI and growth parameters combined with optical and radar remote sensing data, and proposes a data-based Active and passive remote sensing inversion method of maize LAI and biomass based on augmentation and deep learning, by proposing a data augmentation (Beta hybrid method) to expand the number of field measured samples, and constructing a gating mechanism Siamese neural network model , so that the deep learning method with better nonlinear fitting ability is applied to the active and passive inversion of corn LAI and biomass, and the inversion accuracy is improved

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