Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data and Evaluation
2.2. Methodology
2.2.1. Image Processing
2.2.2. Feature Extraction
2.2.3. Model Construction
- (1)
- Feature-Enhancement Module
- (2)
- Region Correction Module
2.2.4. Evaluation Indicators
3. Results
3.1. Results of Feature Extraction
3.2. Detection Results
3.3. Estimation of Contaminated Area and Determination of Contamination Level
4. Discussion
4.1. Low-Altitude Remote-Sensing Technology
4.2. Improved Vegetation Index
4.3. Improved Transformer Model
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Province and City of Sampling | Specific Locations | Number of Images/Sheet |
---|---|---|
Kunming, Yunnan | Daqing River | 708 |
Baoxiang River | 1080 | |
Haigeng Park | 558 | |
Suzhou, Jiangsu | Xujiang River | 1782 |
Jinji Lake | 564 | |
Youlian New Village | 108 | |
Huzhou, Zhejiang | Taihu Lake | 510 |
Maoer Port | 90 |
Scenes | NDVI | NDVIsqrt | ||
---|---|---|---|---|
PA | IoU | PA | IoU | |
Regular | 79.5% | 0.71 | 81.6% | 0.76 |
Reflections | 75.3% | 0.64 | 86.4% | 0.79 |
Shadows | 82.2% | 0.79 | 87.6% | 0.82 |
River | NDVI | NDVIsqrt | ||
---|---|---|---|---|
PA | IoU | PA | IoU | |
Daqing River | 76.1% | 0.64 | 89.4% | 0.76 |
Baoxiang River | 63.4% | 0.58 | 81.3% | 0.86 |
Xujiang River | 85.3% | 0.85 | 92.3% | 0.91 |
Haigeng Park | 56.4% | 0.57 | 82.3% | 0.76 |
Scenes | FCN | SegNet | Improved Transformer | |||
---|---|---|---|---|---|---|
PA | IoU | PA | IoU | PA | IoU | |
Regular | 72% | 0.75 | 75.4% | 0.72 | 87.6% | 0.82 |
Reflections | 61% | 0.56 | 60.9% | 0.62 | 81.6% | 0.76 |
Shadows | 59.3% | 0.62 | 65.7% | 0.73 | 86.4% | 0.79 |
Levels | Proportion—P (%) | Characteristics |
---|---|---|
I | 0 | No cyanobacterial blooms |
II | 0 < P < 10 | No obvious cyanobacterial blooms |
III | 10 ≤ P < 30 | Mild cyanobacterial blooms |
IV | 30 ≤ P < 60 | Moderate cyanobacterial blooms |
V | 60 ≤ P ≤ 100 | Severe cyanobacterial blooms |
River | NDVI | Proposed Method | True Value | |||
---|---|---|---|---|---|---|
Percentage | Level | Percentage | Level | Percentage | Level | |
Daqing River | 65.9% | V | 48.7% | IV | 47.9% | IV |
Xujiang River | 20.2% | III | 21.8% | III | 20.5% | III |
Jinji Lake | 91.6% | V | 77.6% | V | 79.4% | V |
Baoxiang River | 85.2% | V | 79.6% | V | 78.9% | V |
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Song, Z.; Xu, W.; Dong, H.; Wang, X.; Cao, Y.; Huang, P.; Hou, D.; Wu, Z.; Wang, Z. Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method. Sensors 2022, 22, 4571. https://doi.org/10.3390/s22124571
Song Z, Xu W, Dong H, Wang X, Cao Y, Huang P, Hou D, Wu Z, Wang Z. Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method. Sensors. 2022; 22(12):4571. https://doi.org/10.3390/s22124571
Chicago/Turabian StyleSong, Ze, Wenxin Xu, Huilin Dong, Xiaowei Wang, Yuqi Cao, Pingjie Huang, Dibo Hou, Zhengfang Wu, and Zhongyi Wang. 2022. "Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method" Sensors 22, no. 12: 4571. https://doi.org/10.3390/s22124571
APA StyleSong, Z., Xu, W., Dong, H., Wang, X., Cao, Y., Huang, P., Hou, D., Wu, Z., & Wang, Z. (2022). Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method. Sensors, 22(12), 4571. https://doi.org/10.3390/s22124571