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Your search keyword '"Mao, Zhihua"' showing total 11 results
Start Over Author "Mao, Zhihua" Journal ieee transactions on geoscience and remote sensing
11 results on '"Mao, Zhihua"'

2. Benthic Mapping of Coral Reef Areas at Varied Water Depths Using Integrated Active and Passive Remote Sensing Data and Novel Visual Transformer Models

Author

Zhou, Yan, Mao, Zhihua, Mao, Zexi, Zhang, Xianliang, Zhang, Longwei, and Huang, Haiqing

Abstract

In recent years, various coral reef retrieval methods have experienced considerable progress due to a variety of observational instruments and innovative parameter calculation techniques. However, these labor-intensive methods are deficient in handling high-precision remote sensing mapping of coral reef benthic environments, facing challenges, including enhancing the robustness of scaled coral reef retrieval against varying water depths and complex water column conditions. To overcome these limitations, we propose a novel method for coral reef benthic mapping. Our method primarily consists of two central components: water depth extraction and coral reef information acquisition. Accurate bathymetry is critical for coral reef remote sensing inversion. To obtain more precise water depth, we propose the Bathymetry Transformer model. Our Bathymetry Transformer model aggregates vast amounts of active and passive remote sensing data, generates accurate bathymetry (with a 0.375-m RMSE, ranging from 0- to 12-m water depth) that eliminates the need for in situ examinations, and maintains a harmonious balance between greater bathymetry precision and finer spatial resolution. Based on this, water column corrections are then applied, and the results derived from various active and passive remote sensing processes, along with their respective band calculation outcomes, are fed into the proposed coral reef Transformer (CR Transformer) model to generate high-accuracy coral reef benthic mapping results. Copious experimental outcomes affirm that our CR Transformer surpasses current state-of-the-art (SOTA) methods in computational efficiency and results accuracy. Impressively, the CR Transformer achieves a notable mean intersection over union (mIoU) of 91.25% and an accuracy of 95.71% on the validation dataset.

Published
2024
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9. Lidar Remote Sensing of Seawater Optical Properties: Experiment and Monte Carlo Simulation

Author

Liu, Dong, primary, Chen, Peng, additional, Che, Haochi, additional, Liu, Zhipeng, additional, Liu, Qun, additional, Song, Qingjun, additional, Chen, Sijie, additional, Xu, Peituo, additional, Zhou, Yudi, additional, Chen, Weibiao, additional, Han, Bing, additional, Zhu, Xiaolei, additional, He, Yan, additional, Mao, Zhihua, additional, and Le, Chengfeng, additional

Published
2019
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