Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model
Abstract
:1. Introduction
2. Material and Methods
2.1. Overview of KOMPSAT-3 and KOMPSAT-3A Satellite Datasets
2.2. KOMPSAT-3 and KOMPSAT-3A Dataset Construction Process
2.3. The Network Structure of the Deep Learning Model
- : the output value at coordinate after applying atrous convolution;
- : the pixel value in the input feature map, sampled at intervals defined by the atrous rate
- : the weights of the convolutional filter (kernel);
- : the atrous rate, which determines the spacing between the sampled pixels;
- : the indices of the kernel dimensions.
2.4. Specifications of Hyperparameters
- GT: ground truth from labeled image;
- Pred: predicted cloud detection image from model.
2.5. The Performance Metrics of Cloud Detection
3. Results
3.1. Training and Validation Loss Analysis for Epochs
3.2. Evaluation and Analysis of the Generalization Performance of Datasets
3.3. Evaluation by Channel Configuration
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Satellite | KOMPSAT 3 | KOMPSAT 3A |
---|---|---|
Blue-band wavelength (um) | 450–520 | 450–520 |
Green-band wavelength (um) | 520–600 | 520–600 |
Red-band wavelength (um) | 630–690 | 630–690 |
NIR-band wavelength (um) | 760–900 | 760–900 |
Resolution (m) | 2.8 | 2.2 |
Publicly opened cloud detection dataset | KOMPSAT-3 | KOMPSAT-3A |
Sensor | PAN, MSI | PAN, MSI |
Launch year | 2015 | 2017 |
Data acquisition period | 3 days | 3 days |
Parameters | Configuration |
---|---|
Learning rate for Adam | 0.0003 |
Learning rate factor for scheduling | 0.5 |
Momentum | 0.937 |
Weight decay | 0.0005 |
Channel | RGB | NGR | RGBN | RGBN+NDVI | TOA | TOA+NDVI |
---|---|---|---|---|---|---|
Parameters | 45,670,484 | 45,670,484 | 45,673,620 | 45,676,756 | 45,673,620 | 45,676,756 |
Experiment | Class | Precision | Recall | F1-Score | mIoU |
---|---|---|---|---|---|
RGB | Clear Sky | 90.75% | 97.26% | 93.89% | 88.49% |
Thick Cloud | 48.84% | 95.76% | 64.68% | 47.80% | |
Thin Cloud | 68.35% | 34.63% | 45.97% | 29.84% | |
Cloud Shadow | 56.18% | 22.28% | 31.91% | 18.98% | |
Overall | 66.03% | 62.48% | 59.11% | 46.28% | |
NGR | Clear Sky | 89.75% | 97.20% | 93.33% | 87.49% |
Thick Cloud | 48.33% | 90.75% | 63.07% | 46.06% | |
Thin Cloud | 64.88% | 27.60% | 38.73% | 24.02% | |
Cloud Shadow | 62.92% | 27.85% | 38.61% | 23.92% | |
Overall | 66.47% | 60.85% | 58.43% | 45.37% | |
RGBN | Clear Sky | 89.76% | 97.86% | 93.63% | 88.02% |
Thick Cloud | 49.92% | 92.76% | 64.91% | 48.05% | |
Thin Cloud | 70.76% | 26.79% | 38.87% | 24.12% | |
Cloud Shadow | 61.80% | 27.24% | 37.82% | 23.32% | |
Overall | 68.06% | 61.16% | 58.80% | 45.88% | |
RGBN+NDVI | Clear Sky | 89.87% | 96.84% | 93.22% | 87.31% |
Thick Cloud | 44.19% | 92.79% | 59.87% | 42.73% | |
Thin Cloud | 65.55% | 27.43% | 38.67% | 23.97% | |
Cloud Shadow | 50.87% | 23.76% | 32.39% | 19.33% | |
Overall | 62.62% | 60.20% | 56.04% | 43.33% | |
TOA | Clear Sky | 90.12% | 97.91% | 93.85% | 88.42% |
Thick Cloud | 48.27% | 88.40% | 62.44% | 45.39% | |
Thin Cloud | 71.75% | 29.99% | 42.30% | 26.82% | |
Cloud Shadow | 59.43% | 24.68% | 34.87% | 21.12% | |
Overall | 67.39% | 60.24% | 58.37% | 45.44% | |
TOA+NDVI | Clear Sky | 90.19% | 97.58% | 93.74% | 88.22% |
Thick Cloud | 51.89% | 86.88% | 64.97% | 48.12% | |
Thin Cloud | 69.18% | 35.16% | 46.62% | 30.40% | |
Cloud Shadow | 66.06% | 20.89% | 31.75% | 18.87% | |
Overall | 69.33% | 60.13% | 59.27% | 46.40% |
group1 | group2 | Clear Sky | Thick Cloud | Thin Cloud | Cloud Shadow | ||||
---|---|---|---|---|---|---|---|---|---|
MD | p-adj | MD | p-adj | MD | p-adj | MD | p-adj | ||
NGR | RGB | 0.002 | 0 | 0.0507 | 0 | 0.0723 | 0 | −0.0432 | 0 |
NGR | RGBN | 0.008 | 0 | −0.0024 | 0.0966 | −0.015 | 0 | −0.0011 | 0.9723 |
NGR | RGBNNDVI | 0 | 0 | 0.018 | 0 | −0.016 | 0 | −0.0204 | 0 |
NGR | TOA | 0.01 | 0 | −0.0305 | 0 | 0.014 | 0 | −0.022 | 0 |
NGR | TOANDVI | 0.005 | 0 | −0.0354 | 0 | 0.0741 | 0 | −0.058 | 0 |
RGB | RGBN | 0.007 | 0 | −0.0531 | 0 | −0.088 | 0 | 0.0422 | 0 |
RGB | RGBNNDVI | 0 | 0 | −0.0327 | 0 | −0.089 | 0 | 0.0228 | 0 |
RGB | TOA | 0.008 | 0 | −0.0812 | 0 | −0.058 | 0 | 0.0212 | 0 |
RGB | TOANDVI | 0.004 | 0 | −0.0861 | 0 | 0.0018 | 0.58 | −0.0148 | 0 |
RGBN | RGBNNDVI | −0.01 | 0 | 0.0204 | 0 | −0.001 | 0.9495 | −0.0194 | 0 |
RGBN | TOA | 0.001 | 0 | −0.0281 | 0 | 0.0294 | 0 | −0.0209 | 0 |
RGBN | TOANDVI | 0 | 0 | −0.033 | 0 | 0.0895 | 0 | −0.057 | 0 |
RGBNNDVI | TOA | 0.011 | 0 | −0.0485 | 0 | 0.0304 | 0 | −0.0016 | 0.858 |
RGBNNDVI | TOANDVI | 0.007 | 0 | −0.0534 | 0 | 0.0905 | 0 | −0.0376 | 0 |
TOA | TOANDVI | 0 | 0 | −0.0049 | 0 | 0.0601 | 0 | −0.036 | 0 |
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Kim, S.; Han, D.; Lee, Y.; Doo, E.; Oh, H.; Ko, J.; Yeom, J. Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model. Appl. Sci. 2025, 15, 4339. https://doi.org/10.3390/app15084339
Kim S, Han D, Lee Y, Doo E, Oh H, Ko J, Yeom J. Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model. Applied Sciences. 2025; 15(8):4339. https://doi.org/10.3390/app15084339
Chicago/Turabian StyleKim, Suhwan, Doehee Han, Yejin Lee, Eunsu Doo, Han Oh, Jonghan Ko, and Jongmin Yeom. 2025. "Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model" Applied Sciences 15, no. 8: 4339. https://doi.org/10.3390/app15084339
APA StyleKim, S., Han, D., Lee, Y., Doo, E., Oh, H., Ko, J., & Yeom, J. (2025). Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model. Applied Sciences, 15(8), 4339. https://doi.org/10.3390/app15084339