A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information
Abstract
:1. Introduction
2. Related Work
2.1. Spatial-Based Methods
2.2. Spectral-Based Methods
2.3. Temporal-Based Methods
2.4. Hybrid-Based Methods
3. Methodology
3.1. A Multilayer Perceptron with a Presingle-Connection Layer (SMLP)
3.2. Conditional Information
3.2.1. Temporal and Spectral Information
3.2.2. Spatial Information
3.2.3. Pixel Similarity
3.3. Cloud Removal Method Constrained by Conditional Information (SMLP-CR)
3.3.1. Mask Generation
3.3.2. Model Training
3.3.3. Cloud Removal
3.3.4. Image Synthesis
4. Experiments
4.1. Datasets
4.1.1. Data Sources
4.1.2. Data Preprocessing
4.2. Evaluation Metrics and Implementation Details
4.3. Simulated Experiments using Various Temporal RS Images
4.4. Comparison Experiments and Discussions
4.4.1. Comparison Methods and Hyperparameters
4.4.2. Simulated Comparison Experiments
4.4.3. Real-World Comparison Experiments
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Data Source | Path/Row | Date of Restoration Images | Date of Conditional Images | Cloud-Covered Percentage of Applications/Simulations |
---|---|---|---|---|---|
Suzhou | Landsat 8-9 OLI-TIRS | 119/038 | 18 June 2022 | 9 November 2021, 26 February 2022, 28 May 2023 | 43.2%/48.8% |
Kuqa | Landsat 8-9 OLI-TIRS | 145/031 | 1 October 2023 | 4 February 2022, 17 December 2022, 2 January 2023 | 33.5%/34.4% |
Guangzhou | Sentinel-2 MSI | N0400/R089/T51STR | 23 February 2023 | 16 October 2023, 20 November 2023, 25 December 2023 | 27.7%/22.0% |
Conditional Information | SSIM↑ | PSNR↓ | ||||
---|---|---|---|---|---|---|
SWIR | NIR | RED | SWIR | NIR | RED | |
OLI-20211109 | 0.9147 | 0.9196 | 0.9650 | 39.11 | 38.91 | 44.31 |
OLI-20220226 | 0.9178 | 0.9038 | 0.9687 | 39.58 | 38.66 | 44.81 |
OLI-20230528 | 0.9271 | 0.9302 | 0.9713 | 40.17 | 39.84 | 45.25 |
MSI-20220227 | 0.8942 | 0.8812 | 0.9611 | 38.31 | 37.91 | 43.98 |
Multi-Temporal | 0.9380 | 0.9458 | 0.9765 | 40.79 | 40.67 | 46.05 |
Conditional Information | OLI-20211109 | OLI-20220226 | OLI-20230528 | MSI-20220227 |
---|---|---|---|---|
Feature Importance | 0.25 | 0.28 | 0.30 | 0.17 |
Method | Dataset | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Suzhou | Kuqa | Guangzhou | ||||||||||||||||
SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | |||||||||||||
SWIR | NIR | RED | SWIR | NIR | RED | SWIR | NIR | RED | SWIR | NIR | RED | SWIR | NIR | RED | SWIR | NIR | RED | |
GAN (TGRS, 2021) [28] | 0.7442 | 0.7237 | 0.8948 | 34.51 | 34.19 | 38.85 | 0.8751 | 0.8825 | 0.9079 | 46.06 | 46.04 | 46.97 | 0.8440 | 0.8506 | 0.8696 | 45.60 | 45.59 | 45.60 |
LLHM (USGS, 2005) [33] | 0.8871 | 0.8582 | 0.9546 | 36.97 | 36.03 | 40.67 | 0.9642 | 0.9620 | 0.9681 | 50.01 | 49.85 | 50.74 | 0.9558 | 0.9498 | 0.9562 | 47.75 | 47.78 | 49.33 |
STS-CNN (TGRS, 2018) [23] | 0.9207 | 0.9233 | 0.9665 | 38.38 | 37.99 | 43.36 | 0.9754 | 0.9742 | 0.9811 | 50.88 | 51.07 | 52.17 | 0.9681 | 0.9601 | 0.9628 | 49.37 | 48.80 | 49.96 |
STGAN (WACV, 2020) [12] | 0.8596 | 0.8474 | 0.9369 | 35.91 | 35.19 | 40.35 | 0.9598 | 0.9571 | 0.9671 | 47.89 | 47.35 | 48.68 | 0.9560 | 0.9438 | 0.9446 | 47.69 | 47.32 | 48.13 |
SEN12MS-CR (TGRS, 2022) [15] | 0.8953 | 0.8935 | 0.9512 | 37.18 | 36.64 | 41.18 | 0.9700 | 0.9669 | 0.9751 | 49.93 | 49.58 | 50.95 | 0.9623 | 0.9477 | 0.9496 | 48.47 | 47.70 | 48.61 |
CTGAN (ICIP, 2022) [14] | 0.8747 | 0.8868 | 0.9443 | 36.65 | 35.98 | 41.83 | 0.9710 | 0.9693 | 0.9760 | 49.66 | 49.58 | 50.59 | 0.9663 | 0.9535 | 0.9562 | 48.62 | 48.03 | 48.79 |
PMAA (ECAI, 2023) [42] | 0.8981 | 0.9036 | 0.9580 | 37.11 | 36.65 | 42.60 | 0.9752 | 0.9710 | 0.9790 | 50.13 | 48.62 | 50.82 | 0.9660 | 0.9507 | 0.9564 | 48.82 | 47.95 | 49.30 |
DiffCR (TGRS, 2024) [8] | 0.9109 | 0.9153 | 0.9549 | 38.22 | 37.75 | 42.04 | 0.9731 | 0.9703 | 0.9758 | 50.53 | 49.86 | 51.01 | 0.9658 | 0.9542 | 0.9539 | 48.68 | 48.05 | 48.55 |
SMLP-CR | 0.9266 | 0.9307 | 0.9710 | 40.19 | 39.88 | 45.18 | 0.9804 | 0.9786 | 0.9843 | 53.00 | 52.90 | 54.45 | 0.9694 | 0.9614 | 0.9653 | 49.23 | 48.82 | 50.11 |
Method | Dataset | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Suzhou | Kuqa | Guangzhou | ||||||||||||||||
SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | |||||||||||||
SWIR | NIR | RED | SWIR | NIR | RED | SWIR | NIR | RED | SWIR | NIR | RED | SWIR | NIR | RED | SWIR | NIR | RED | |
STS-CNN (TGRS, 2018) | 0.9289 | 0.9306 | 0.9718 | 38.91 | 37.98 | 43.80 | 0.9809 | 0.9800 | 0.9852 | 51.98 | 52.66 | 53.44 | 0.9737 | 0.9695 | 0.9701 | 50.18 | 49.84 | 50.93 |
STGAN (WACV, 2020) | 0.9023 | 0.9043 | 0.9596 | 37.01 | 36.40 | 41.64 | 0.9687 | 0.9647 | 0.9744 | 49.04 | 48.17 | 49.94 | 0.9660 | 0.9611 | 0.9610 | 48.25 | 48.18 | 49.21 |
SEN12MS-CR (TGRS, 2022) | 0.9157 | 0.9242 | 0.9664 | 38.23 | 37.79 | 42.58 | 0.9756 | 0.9734 | 0.9797 | 51.79 | 51.51 | 52.56 | 0.9756 | 0.9648 | 0.9695 | 50.01 | 49.00 | 50.45 |
CTGAN (ICIP, 2022) | 0.9040 | 0.9120 | 0.9586 | 37.51 | 37.04 | 42.24 | 0.9751 | 0.9730 | 0.9797 | 50.88 | 50.54 | 51.69 | 0.9722 | 0.9672 | 0.9690 | 49.12 | 48.86 | 50.14 |
PMAA (ECAI, 2023) | 0.9188 | 0.9315 | 0.9651 | 39.22 | 38.82 | 42.44 | 0.9792 | 0.9781 | 0.9834 | 51.66 | 51.96 | 52.13 | 0.9726 | 0.9695 | 0.9729 | 49.76 | 49.50 | 51.13 |
DiffCR (TGRS, 2024) | 0.9180 | 0.9288 | 0.9608 | 38.64 | 38.45 | 42.62 | 0.9765 | 0.9737 | 0.9790 | 51.95 | 51.03 | 51.77 | 0.9753 | 0.9686 | 0.9706 | 50.08 | 49.53 | 50.65 |
SMLP-CR | 0.9363 | 0.9449 | 0.9753 | 40.76 | 40.74 | 45.98 | 0.9848 | 0.9834 | 0.9882 | 55.16 | 55.06 | 56.59 | 0.9779 | 0.9745 | 0.9773 | 50.85 | 50.54 | 52.11 |
Method | Model Complexity | |
---|---|---|
Floating Point Operations (FLOPs) | Parameters | |
STS-CNN (TGRS, 2018) | ||
STGAN (WACV, 2020) | ||
SEN12MS-CR (TGRS, 2022) | ||
CTGAN (ICIP, 2022) | ||
PMAA (ECAI, 2023) | ||
DiffCR (TGRS, 2024) | ||
SMLP-CR |
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Zhang, W.; Zhang, H.; Zhang, X.; Shen, X.; Zou, L. A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information. Remote Sens. 2024, 16, 3134. https://doi.org/10.3390/rs16173134
Zhang W, Zhang H, Zhang X, Shen X, Zou L. A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information. Remote Sensing. 2024; 16(17):3134. https://doi.org/10.3390/rs16173134
Chicago/Turabian StyleZhang, Wenyi, Haoran Zhang, Xisheng Zhang, Xiaohua Shen, and Lejun Zou. 2024. "A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information" Remote Sensing 16, no. 17: 3134. https://doi.org/10.3390/rs16173134