Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model

Du, Kai; Huang, Huaguo; Zhu, Yuyi; Feng, Ziyi; Hakala, Teemu; Chen, Yuwei; Hyyppä, Juha

doi:10.3390/rs12040684

Cited by 4 publications

(11 citation statements)

References 34 publications

(35 reference statements)

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“…The authors are sorry to report that there are seven mistakes in this paper [1]. On page 7, lines 28, the location of Evo "61 • 19 N, 25 • 11 S" should be "61 • 19 N, 25 • 11 E".…”

Section: Change In Main Body Paragraphsmentioning

confidence: 96%

“…Due to a lapse, the Acknowledgement section was missing in the original article version [1]. The authors wish to add the section for sufficient acknowledgments:…”

Section: Change In Acknowledgementmentioning

confidence: 99%

“…In the original version of our paper [1], insufficient acknowledgement was given for the source of Tomoradar and lidar data used. We apologize for the original error.…”

Section: Change In Author Names (Add Four New Authors)mentioning

confidence: 99%

“…We apologize for the original error. To correct this oversight, we wish to add Ziyi Feng, Teemu Hakala, Yuwei Chen and Juha Hyyppä as authors of our paper [1]. The updated list of the authors of this paper and their affiliations are provided below:…”

Section: Change In Author Names (Add Four New Authors)mentioning

confidence: 99%

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Erratum: Du, K., et al. Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model. Remote Sensing 2020, 12, 684

Huang

Zhu

et al. 2020

Remote Sensing

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“…The authors are sorry to report that there are seven mistakes in this paper [1]. On page 7, lines 28, the location of Evo "61 • 19 N, 25 • 11 S" should be "61 • 19 N, 25 • 11 E".…”

Section: Change In Main Body Paragraphsmentioning

confidence: 96%

“…Due to a lapse, the Acknowledgement section was missing in the original article version [1]. The authors wish to add the section for sufficient acknowledgments:…”

Section: Change In Acknowledgementmentioning

confidence: 99%

“…In the original version of our paper [1], insufficient acknowledgement was given for the source of Tomoradar and lidar data used. We apologize for the original error.…”

Section: Change In Author Names (Add Four New Authors)mentioning

confidence: 99%

Section: Change In Author Names (Add Four New Authors)mentioning

confidence: 99%

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Erratum: Du, K., et al. Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model. Remote Sensing 2020, 12, 684

Huang

Zhu

et al. 2020

Remote Sensing

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“…Tomoradar can provide four linear polarization measuring capabilities in Ku band based bistatic configuration with 15 cm range solution, to improve the understanding of the radar backscatter response for forestry mapping and inventories. Subsequent studies on forest inventory, especially for vertical forest structure using FMCW radar data have been conducted in the past few years [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The Determination of Effective Beamwidth of Ku Band Profiling Radar Based on Waveform Matching Method in the Boreal Forest of Finland

Zhou

Chen

Hu³

et al. 2020

Remote Sensing

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Radar scientists typically define the radar beamwidth as a half-power beamwidth (HPBW) in the main lobe of the antenna pattern. However, the microwave radiations outside radar HPBW might also backscatter into the radar receiver and change the distribution of the received signal. To determine an actual and effective beamwidth illuminated on the measured targets, we first generate the simulated-waveforms derived from coincident lidar points and radar equation and then develop a waveform matching method to seek out an optimal beamwidth based on the 95% threshold of correlation coefficients between radar waveforms and the simulated-waveforms. The 8565 measurements of a Ku-band profiling radar named Tomoradar and coincident lidar data in a widespread heterogeneous forest area of southern Finland are employed for resolving the effective beamwidth. The results reveal that about 97% of the effective beamwidth are larger than Tomoradar HPBW, but the effective beamwidth could be changeable for each measurement due to variations in the scattering properties of vegetation. Thus, a fixed average effective beamwidth (AEBW) with 0.1-degree resolution is introduced to determine Tomoradar cone according to the effective beamwidth and corresponding proportions. We discover that Tomoradar AEBW is approximately approaching to 8°, which is larger than Tomoradar HPBW of 6°. If we regard AEBW as the actual Tomoradar beamwidth rather than HPBW, the simulated-waveforms have substantially stronger correlation strength with Tomoradar waveforms, and canopy tops derived from lidar data within Tomoradar AEBW are much closer to those extracted from Tomoradar waveforms. The results demonstrate that radar AEBW is a more appropriate reference for designing radar antenna and selecting the region size of validation data such as lidar points or the ground truth. However, considering that radar AEBW is variable for different radar antenna pattern, we suggest that actual radar beamwidth should be defined with a fraction of total radiation energy within radar AEBW, just like the definition of laser divergence of lidar based on the percentage of transmitted laser energy. In this paper, for a forest inventory research case, the fraction of total radiation energy within the AEBW for radar system is supposed to be 91%.

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

Jin¹,

Qi²,

Huang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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 study, we proposed a hybrid model, which combines a 3D 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 finetuning 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, R 2 = 0.83), compared with RF (RMSE = 0.26, R 2 = 0.61), Data-CNN (RMSE = 0.142, R 2 = 0.81), and RTM-CNN (RMSE = 0.144, R 2 = 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.

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Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model

Cited by 4 publications

References 34 publications

Erratum: Du, K., et al. Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model. Remote Sensing 2020, 12, 684

Erratum: Du, K., et al. Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model. Remote Sensing 2020, 12, 684

The Determination of Effective Beamwidth of Ku Band Profiling Radar Based on Waveform Matching Method in the Boreal Forest of Finland

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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