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Your search keyword '"Tang, Hongzhao"' showing total 31 results
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31 results on '"Tang, Hongzhao"'

3. Radiometric Cross-Calibration of GF1-B/C/D Satellite with Landsat-8 OLI

Author

Wang, Hengyang, Tang, Hongzhao, Zhang, Honggeng, Hui, Chen, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wang, Liheng, editor, Wu, Yirong, editor, and Gong, Jianya, editor

Published
2023
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4. Validation of ZY1-02D Satellite Multispectral Sensor on-Orbit Radiometric Calibration Accuracy

Author

Tang, Hongzhao, Zhang, Honggeng, Hui, Chen, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wang, Liheng, editor, Wu, Yirong, editor, and Gong, Jianya, editor

Published
2023
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14. Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

Author

Dou, Xianhui, primary, Li, Kun, additional, Zhang, Qi, additional, Ma, Chenyang, additional, Tang, Hongzhao, additional, Liu, Xining, additional, Qian, Yonggang, additional, Chen, Jun, additional, Li, Jinglun, additional, Li, Yichao, additional, Wang, Tao, additional, Wang, Feng, additional, and Yang, Juntao, additional

Published
2024
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17. Estimation of Land Surface Temperature from CHINESE ZY1-02E IRS Data

Author

Dou, Xianhui, primary, Li, Kun, additional, Zhang, Qi, additional, Ma, Chenyang, additional, Tang, Hongzhao, additional, Qian, Yonggang, additional, Chen, Jun, additional, Li, Jinglun, additional, Li, Yichao, additional, Wang, Tao, additional, Wang, Feng, additional, Yang, Juntao, additional, and Liu, Xining, additional

Published
2023
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20. Retrieval of an On-Orbit Bidirectional Reflectivity Reference in the Mid-Infrared Bands of FY-3D/MERSI-2 Channels 20.

Author

Peng, Bo, Chen, Wei, Wang, Hengyang, Hu, Xiuqing, Tang, Hongzhao, Li, Guangchao, and Zhang, Fengjiao

Subjects
REFLECTANCE spectroscopy, WATER vapor, WEATHER, PROBLEM solving
Abstract

The acquisition of high-accuracy reflectance in mid-infrared channels is of great significance for the on-orbit cross-calibration of other bands using the mid-infrared band. However, due to the phenomenon that some sensors have a wide range of wavelengths covered by adjacent channels in the mid-infrared band, the traditional method of estimating the mid-infrared reflectivity assumes that the sea surface reflectivity in different mid-infrared bands is equal, which will lead to a large error during calculation. To solve this problem, this study proposes a nonlinear split-window algorithm involving ocean sun glint data to retrieve reflectivity of FY-3D/MERSI-2 channels 20. The results show that the variation range of sea surface reflectivity of channel 20 in the glint area is 10~25%, the mean value of the reflectivity difference obtained by the nonlinear split-window algorithm is 0.27%, and the RMSE is 0.0066. Among the main influencing factors, the atmospheric conditions have the greatest impact, and the effects of the uncertainties in the water vapor content and aerosol optical thickness on the calculation results are 1.16% and 0.34%, respectively. The initial value limits of the mid-infrared sea surface reflectivity also contribute approximately 0.84%, and their contribution to the uncertainty represents one of the main components. This work shows that the nonlinear split-window algorithm can calculate the infrared sea surface reflectivity with high accuracy and can be used as a reference for in-orbit cross-calibration between different bands. [ABSTRACT FROM AUTHOR]

Published
2023
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23. MultiSenseSeg: A Cost-Effective Unified Multimodal Semantic Segmentation Model for Remote Sensing

Author

Wang, Qingpeng, Chen, Wei, Huang, Zhou, Tang, Hongzhao, and Yang, Lan

Abstract

Semantic segmentation is an essential technique in remote sensing. Until recently, most related research has focused primarily on advancing semantic segmentation models based on monomodal imagery, and less attention has been given to models that utilize multimodal remote sensing data. Moreover, most current multimodal approaches consider only limited bimodal situations and cannot simultaneously utilize three or more modalities. The increase in expensive computational costs associated with previous feature fusion paradigms hinders their application in broader cases. How to design a unified method to cover a wide variety of quantity-agnostic modalities for multimodal semantic segmentation remains an unsolved issue. To address the aforementioned challenges, this study explores a feasible way and proposes a cost-effective multimodal sensing semantic segmentation model (MultiSenseSeg). MultiSenseSeg employs multiple lightweight modality-specific experts (MSEs), an adaptive multimodal matching (AMM) module, and a single feature extraction pipeline to efficiently model intramodal and intermodal relationships. Benefiting from these designs, the proposed MultiSenseSeg can serve as a unified multimodal model capable of addressing both monomodal and bimodal cases and readily extrapolating to scenarios with more modalities, thereby achieving semantic segmentation of arbitrary quantities of multimodal data. To evaluate the performance of our method, we select several state-of-the-art (SOTA) semantic segmentation models from the past three years and conduct extensive experiments on two public multimodal datasets. The results show that MultiSenseSeg can not only achieve higher accuracy but also exhibit user-friendly modality extrapolation, allowing end-to-end training for consumer-grade users based on limited hardware resources. The model’s code will be available at https://github.com/W-qp/MultiSenseSeg.

Published
2024
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