Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)
Abstract
:1. Introduction
2. Data and Methods
2.1. Geostationary Ocean Color Imager (GOCI) Data
2.2. Speckle Detection Based on Deep Neural Network Approach
2.3. Construction of Dataset
2.4. Statistical Errors
3. Results
3.1. Speckles from Annual Maximum
3.2. Abnormal Chlorophyll-a Features around Clouds
3.3. Dual Structure of Speckles
3.4. Spectral Characteristics of Speckles
3.5. Implementation of Multilayer Feedforward Neural Network (MFNN) Model
3.6. Effect of De-Speckled Chlorophyll-a Concentration Data on Composite Field
3.7. Validation
4. Discussion
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Class | Precision | Sensitivity | Accuracy |
---|---|---|---|
Normal | 0.917 | 0.880 | 0.889 |
High | 0.857 | 0.882 | 0.857 |
Low | 0.909 | 0.968 | 0.911 |
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Park, J.-E.; Park, K.-A. Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017). Remote Sens. 2021, 13, 585. https://doi.org/10.3390/rs13040585
Park J-E, Park K-A. Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017). Remote Sensing. 2021; 13(4):585. https://doi.org/10.3390/rs13040585
Chicago/Turabian StylePark, Ji-Eun, and Kyung-Ae Park. 2021. "Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)" Remote Sensing 13, no. 4: 585. https://doi.org/10.3390/rs13040585