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Yingzhuo Hou

As seen in: MDPI
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Articles

Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin

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By Yingzhuo Hou, Yonggang Ma, Zheng Hou, Maham Arif
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Monitoring Suspended Sediment Concentration in the Yellow River Estuary and Its Vicinity Waters on the Basis of SDGSAT-1 Multispectral Imager

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By Yingzhuo Hou, Qianguo Xing, Xiangyang Zheng, Dezhi Sheng
| MDPI
3.
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A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning

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By Qianguo Xing, Hailong Liu, Jinghu Li, Yingzhuo Hou
| MDPI
4. Results and Discussion 4.1. Ulva pertusa Extraction Performance from the UAV Images To assess the effectiveness of the proposed U. pertusa extraction method in high-resolution UAV imagery, a comparative analysis was conducted against threshold-based extraction methods utilizing NGBDI and the R band on the test dataset. Table 2 provides an overview of the accuracy evaluation metrics employed for U. pertusa extraction, including the U-Net model, NGBDI, and the R band threshold-based method.
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