Ms. Meihui Song Profile

Meihui Song

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Proceedings Article | 19 January 2024 Paper

An intelligent extraction method for production and construction projects

Mengcheng Hu, Binbin Nie, Shifeng Huang, Yayong Sun, Meihui Song, Xinyu Chen

Proceedings Volume 12980, 1298026 (2024) https://doi.org/10.1117/12.3021230

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Rapidly acquiring disturbed patches in production and construction projects through high-resolution remote sensing images holds significant importance for enhancing soil and water conservation supervision capabilities and controlling human-induced soil erosion. Traditional visual interpretation methods for identifying disturbed patches require substantial effort and time, leading to numerous limitations. To improve the efficiency of soil and water conservation supervision, this paper analyzes and summarizes the change characteristics of production and construction projects between two periods of remote sensing images. An intelligent extraction method for disturbed patches in these projects is proposed, based on deep learning and high-resolution remote sensing images. The U-Net++ architecture is employed as sub-network to construct a Siamese network model, with the integration of an attention mechanism module to enhance model performance. Experimental results in the validation area demonstrate that the proposed method achieves a detection rate of 91.52% for disturbed patches, with a false-negative rate of 8.48%. This outperforms the disturbance patch detection rate of 87.28% and a false-negative rate of 12.72% achieved by the dual-temporal early fusion strategy. The extracted boundaries of disturbed patches closely align with manually annotated patch boundaries, indicating the feasibility of utilizing deep learning for extracting disturbed patches in production and construction projects. This approach offers a novel perspective to enhance the efficiency of soil and water conservation supervision.

Proceedings Article | 30 August 2023 Paper

Dynamic monitoring and spatiotemporal change analysis of Chaohu Lake algal bloom based on Sentinel-2 remote sensing data

Xinyu Chen, Shifeng Huang, Meihui Song, Pu Zheng, Mengcheng Hu

Proceedings Volume 12797, 1279725 (2023) https://doi.org/10.1117/12.3007404

KEYWORDS: Environmental monitoring, Remote sensing, Water quality, Satellites, Near infrared, Reflectivity, Water, Vegetation, Satellite imaging

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To study the patterns of algal blooms in Chaohu Lake, this article uses Sentinel-2 images as the data source and extracts algal bloom using the Normalized Difference Vegetation Index (NDVI). From 2019 to 2022, the dynamic monitoring of algal bloom in Chaohu Lake is conducted, analyzing the temporal and spatial changes in algal blooms, exploring the driving factors of algal blooms in Chaohu Lake. The results indicate that: (1) From 2019 to 2022, the scale of the annual outbreak of algal bloom in Chaohu Lake gradually decreased. The degree of water blooms significantly decreased. (2)Chaohu Lake has strong seasonal changes in water blooms, with severe outbreaks in summer and autumn when temperatures are higher, indicating that temperature is the main factor driving the outbreak of water blooms. (3) As the main pollutant discharge area of Hefei urban area, the northwest of Chaohu Lake is the core area for algal blooms, indicating that water quality is an important influencing factor leading to algal blooms in Chaohu Lake; From the spatial changes in the movement of algal blooms from the northwest and the coast of east lake towards the middle of the lake, and from the middle of the lake to the northwest, it is found that wind speed is more likely to suppress the outbreak of algal blooms.

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