Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models

The polarimetric synthetic aperture radar (PolSAR) can be used to obtain soil moisture by inverting scattering models at high resolution. The convolutional neural network (CNN) has been recently introduced to estimate soil roughness for PolSAR data, which need to be driven by a large amount of data....

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Autores principales:	Qiang Yin, Junlang Li, Fei Ma, Deliang Xiang, Fan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	soil moisture inversion polarimetric synthetic aperture radar deep learning X-Bragg model integral equation model Science Q
Acceso en línea:	https://doaj.org/article/6bd9a50aefb84410a63bcbdee0b09dfc
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spelling	oai:doaj.org-article:6bd9a50aefb84410a63bcbdee0b09dfc2021-11-25T18:53:43ZDual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models10.3390/rs132245032072-4292https://doaj.org/article/6bd9a50aefb84410a63bcbdee0b09dfc2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4503https://doaj.org/toc/2072-4292The polarimetric synthetic aperture radar (PolSAR) can be used to obtain soil moisture by inverting scattering models at high resolution. The convolutional neural network (CNN) has been recently introduced to estimate soil roughness for PolSAR data, which need to be driven by a large amount of data. In this paper, a dual-channel CNN based on polarimetric models is proposed for soil moisture inversion, and it aims to further expand the applicable range of roughness in the X-Bragg model by integration with the integral equation model (IEM). Meanwhile, it fully utilizes the spatial information of PolSAR images to relax the number of required training samples when real data on the surface are difficult to obtain. Besides, we designed a framework based on this network. Coarse-grained inversion and fine-grained inversion of soil moisture are carried out through the qualitative classification network and the quantitative regression network, respectively. Experiments on simulated and airborne E-SAR data show that the proposed network can accurately fit the nonlinear relationship between polarization parameters and soil moisture, so as to improve the inversion accuracy with a small number of samples. In our experiments, the average inversion accuracy reached 95.39%, and the root mean square error (RMSE) of the regression network was 0.98%. This method can be applied to a wide range of soil moisture monitoring applications.Qiang YinJunlang LiFei MaDeliang XiangFan ZhangMDPI AGarticlesoil moisture inversionpolarimetric synthetic aperture radardeep learningX-Bragg modelintegral equation modelScienceQENRemote Sensing, Vol 13, Iss 4503, p 4503 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	soil moisture inversion polarimetric synthetic aperture radar deep learning X-Bragg model integral equation model Science Q
spellingShingle	soil moisture inversion polarimetric synthetic aperture radar deep learning X-Bragg model integral equation model Science Q Qiang Yin Junlang Li Fei Ma Deliang Xiang Fan Zhang Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models
description	The polarimetric synthetic aperture radar (PolSAR) can be used to obtain soil moisture by inverting scattering models at high resolution. The convolutional neural network (CNN) has been recently introduced to estimate soil roughness for PolSAR data, which need to be driven by a large amount of data. In this paper, a dual-channel CNN based on polarimetric models is proposed for soil moisture inversion, and it aims to further expand the applicable range of roughness in the X-Bragg model by integration with the integral equation model (IEM). Meanwhile, it fully utilizes the spatial information of PolSAR images to relax the number of required training samples when real data on the surface are difficult to obtain. Besides, we designed a framework based on this network. Coarse-grained inversion and fine-grained inversion of soil moisture are carried out through the qualitative classification network and the quantitative regression network, respectively. Experiments on simulated and airborne E-SAR data show that the proposed network can accurately fit the nonlinear relationship between polarization parameters and soil moisture, so as to improve the inversion accuracy with a small number of samples. In our experiments, the average inversion accuracy reached 95.39%, and the root mean square error (RMSE) of the regression network was 0.98%. This method can be applied to a wide range of soil moisture monitoring applications.
format	article
author	Qiang Yin Junlang Li Fei Ma Deliang Xiang Fan Zhang
author_facet	Qiang Yin Junlang Li Fei Ma Deliang Xiang Fan Zhang
author_sort	Qiang Yin
title	Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models
title_short	Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models
title_full	Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models
title_fullStr	Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models
title_full_unstemmed	Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models
title_sort	dual-channel convolutional neural network for bare surface soil moisture inversion based on polarimetric scattering models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6bd9a50aefb84410a63bcbdee0b09dfc
work_keys_str_mv	AT qiangyin dualchannelconvolutionalneuralnetworkforbaresurfacesoilmoistureinversionbasedonpolarimetricscatteringmodels AT junlangli dualchannelconvolutionalneuralnetworkforbaresurfacesoilmoistureinversionbasedonpolarimetricscatteringmodels AT feima dualchannelconvolutionalneuralnetworkforbaresurfacesoilmoistureinversionbasedonpolarimetricscatteringmodels AT deliangxiang dualchannelconvolutionalneuralnetworkforbaresurfacesoilmoistureinversionbasedonpolarimetricscatteringmodels AT fanzhang dualchannelconvolutionalneuralnetworkforbaresurfacesoilmoistureinversionbasedonpolarimetricscatteringmodels
_version_	1718410593458716672

Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models

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