Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method

Sensors

卷:22

Zhongyi Wang¹  Zhengfang Wu¹  Huilin Dong²  Yuqi Cao²  Wenxin Xu²  Dibo Hou²  Ze Song²  Xiaowei Wang²  Pingjie Huang² 

[1] City Intelligence, Cloud & AI, Huawei Technologies Co., Ltd., Shenzhen 518100, China;

[2] State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;

关键词: remote-sensing technology;    cyanobacterial blooms;    vegetation index;    deep learning;   

DOI  :  10.3390/s22124571

来源: DOAJ

【 摘 要 】

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.

【 授权许可】

Unknown