Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this article, we proposed...

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Autores principales:	Decai Jin, Jianbo Qi, Huaguo Huang, Linyuan Li
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Three-dimensional (3-D) radiative transfer model convolutional neural network (CNN) forest canopy cover (FCC) transfer learning Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/85399ec30deb4e0aa5a97f8662d06943
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spelling	oai:doaj.org-article:85399ec30deb4e0aa5a97f8662d069432021-11-18T00:00:16ZCombining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images2151-153510.1109/JSTARS.2021.3122509https://doaj.org/article/85399ec30deb4e0aa5a97f8662d069432021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585661/https://doaj.org/toc/2151-1535Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this article, we proposed a hybrid model, which combines a 3-D RTM and transfer learning-based convolutional neural network (T-CNN), to estimate FCC from very high-resolution satellite images (e.g., Chinese GaoFen-2, 1 m resolution with 4 bands). Unlike common hybrid models that are purely trained with simulation data, T-CNN combines simulation data-based pre-training and actual data-based transfer learning, which is a widely used technique in artificial intelligence for fine-tuning models. The performance of T-CNN was compared with a random forest (RF) model and two general CNN models, including CNN trained with actual dataset only (data-CNN) and CNN trained with RTM simulation data only (RTM-CNN). Results on the independent validation dataset (not used in training stage) showed that T-CNN had higher accuracy (RMSE = 0.121, <italic>R</italic><sup>2</sup> = 0.83), compared with RF (RMSE = 0.26, <italic>R</italic><sup>2</sup> = 0.61), Data-CNN (RMSE = 0.142, <italic>R</italic><sup>2</sup> = 0.81), and RTM-CNN (RMSE = 0.144, <italic>R</italic><sup>2</sup> = 0.73), which indicates that T-CNN has a strong transferability. Tests on different training sizes showed that T-CNN (0.084 < RMSE < 0.108) provided constantly better performances than RF (0.116 < RMSE < 0.122) and data-CNN (0.103 < RMSE < 0.128), which demonstrates the potential of T-CNN as an alternative to RTM-based inversion and data-driven regressions to estimate FCC, especially when training data is imbalanced and inadequate.Decai JinJianbo QiHuaguo HuangLinyuan LiIEEEarticleThree-dimensional (3-D) radiative transfer modelconvolutional neural network (CNN)forest canopy cover (FCC)transfer learningOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 10953-10963 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Three-dimensional (3-D) radiative transfer model convolutional neural network (CNN) forest canopy cover (FCC) transfer learning Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Three-dimensional (3-D) radiative transfer model convolutional neural network (CNN) forest canopy cover (FCC) transfer learning Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Decai Jin Jianbo Qi Huaguo Huang Linyuan Li Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images
description	Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this article, we proposed a hybrid model, which combines a 3-D RTM and transfer learning-based convolutional neural network (T-CNN), to estimate FCC from very high-resolution satellite images (e.g., Chinese GaoFen-2, 1 m resolution with 4 bands). Unlike common hybrid models that are purely trained with simulation data, T-CNN combines simulation data-based pre-training and actual data-based transfer learning, which is a widely used technique in artificial intelligence for fine-tuning models. The performance of T-CNN was compared with a random forest (RF) model and two general CNN models, including CNN trained with actual dataset only (data-CNN) and CNN trained with RTM simulation data only (RTM-CNN). Results on the independent validation dataset (not used in training stage) showed that T-CNN had higher accuracy (RMSE = 0.121, <italic>R</italic><sup>2</sup> = 0.83), compared with RF (RMSE = 0.26, <italic>R</italic><sup>2</sup> = 0.61), Data-CNN (RMSE = 0.142, <italic>R</italic><sup>2</sup> = 0.81), and RTM-CNN (RMSE = 0.144, <italic>R</italic><sup>2</sup> = 0.73), which indicates that T-CNN has a strong transferability. Tests on different training sizes showed that T-CNN (0.084 < RMSE < 0.108) provided constantly better performances than RF (0.116 < RMSE < 0.122) and data-CNN (0.103 < RMSE < 0.128), which demonstrates the potential of T-CNN as an alternative to RTM-based inversion and data-driven regressions to estimate FCC, especially when training data is imbalanced and inadequate.
format	article
author	Decai Jin Jianbo Qi Huaguo Huang Linyuan Li
author_facet	Decai Jin Jianbo Qi Huaguo Huang Linyuan Li
author_sort	Decai Jin
title	Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images
title_short	Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images
title_full	Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images
title_fullStr	Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images
title_full_unstemmed	Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images
title_sort	combining 3d radiative transfer model and convolutional neural network to accurately estimate forest canopy cover from very high-resolution satellite images
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/85399ec30deb4e0aa5a97f8662d06943
work_keys_str_mv	AT decaijin combining3dradiativetransfermodelandconvolutionalneuralnetworktoaccuratelyestimateforestcanopycoverfromveryhighresolutionsatelliteimages AT jianboqi combining3dradiativetransfermodelandconvolutionalneuralnetworktoaccuratelyestimateforestcanopycoverfromveryhighresolutionsatelliteimages AT huaguohuang combining3dradiativetransfermodelandconvolutionalneuralnetworktoaccuratelyestimateforestcanopycoverfromveryhighresolutionsatelliteimages AT linyuanli combining3dradiativetransfermodelandconvolutionalneuralnetworktoaccuratelyestimateforestcanopycoverfromveryhighresolutionsatelliteimages
_version_	1718425234928828416

Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

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