Cloud Detection Methods for Optical Satellite Imagery: A Comprehensive Review
Abstract
1. Introduction
- Land Cover Monitoring: RS images help in monitoring changes in land cover (like forest, grassland, urban areas, and agriculture fields), urban growth & expansion (like development of buildings, roads, and other urban infrastructure), deforestation, monitoring mountains & canyons, and change detection [7,8,9].
- Geology and Natural Resource Management: RS is extensively used in locating mineral resources, monitoring mining processes, managing freshwater resources (such as lakes, rivers, and glaciers), and geological surveys for identifying rock formations, faults, folds, and other geological structures [6,14].
- Military Surveillance: RS helps in gathering visual intelligence like enemy activities, troop movement, equipment deployment, and infrastructure changes [15]. It also helps in keeping track of borders and suspicious activities and identifying potential threats. It monitors maritime activities like tracking naval vessels, illegal fishing, and smuggling [16].
- Oceanography: RS aids in estimating ocean currents, marine life distribution, understanding sea dynamics, and climate change effects [17].
2. Cloud and Its Effect
- Low clouds: They are found to be nearly touching the ground and lie within 2 km above ground level (AGL). They are typically formed by the condensation of water droplets, although in high-latitude regions, low clouds can also be formed by ice particles. These clouds usually comprise stratus, stratocumulus, and nimbostratus clouds. They are generally optically thick, which mostly obscures the objects beneath them, leading to dark shadows. Vertical clouds typically extend through a wide range of altitudes, from low to high levels. They are characterized by being optically thick, meaning they significantly block or scatter incoming radiation, similar to low clouds in appearance and effect.
- Medium clouds: These are between low and high clouds (2 km to 6 km) that include altostratus and altocumulus clouds. They are formed due to low temperatures by ice crystals and water droplets [31]. They are slightly less dense than low clouds, but the visibility of objects below them is less than 50%, and they have shadows slightly away from the cloud in satellite imagery. If only two cloud types are considered, they fall in the low cloud category.
- High clouds: They are usually above 6 km and include cirrus, cirrostratus, and cirrocumulus clouds formed by ice crystals in stable air [32]. They are optically thin, light, streaky, and cover large areas. Ground objects are visible but look hazy and blurry due to their presence [33]. Their shadow is usually very light and often gets removed during atmospheric correction of satellite imagery.
- Thick cloud: A cloud is classified as ‘thick’ when it obscures the underlying Earth’s surface with an opacity exceeding 50%, which corresponds to a high optical thickness commonly used in remote sensing studies. Thick clouds make it impossible to comprehend any details below, ultimately hindering the interpretation of ground-level events and creating dark cloud shadows that hide the objects (Figure 3a). The formation of cloud shadows depends on the altitude & thickness of the cloud, the sun angle, and the characteristics of the Earth’s surface over which the shadow is falling [35].
- Thin cloud: In contrast, a cloud is termed ‘thin’ if it covers the surface with less than 50% opacity, corresponding to low optical thickness [36]. It offers limited ground visibility, but the view is often blurred and distorted, making it challenging to accurately perceive the terrestrial activities (Figure 3b).
3. Cloud Detection
- Binary cloud mask: A binary cloud mask usually has only two classes or values, and it has two variants: cloud-only [42,43,44] and cloud-contaminated [45]. In the cloud-only variant, each pixel is categorized as either being under cloud cover or being free from clouds (Figure 4b). On the other hand, the cloud-contaminated variant shares the same classification structure, but the cloudy pixels include each type of cloud as well as cloud shadow (Figure 4c). Cloud-only and cloud-contaminated variants of binary masks can easily be created using multiclass cloud masks [33,40].
- Multiclass cloud mask: A multiclass cloud mask is an improved version of cloud masking, where pixels are classified into several distinct categories [38,44]. Each class signifies different types of cloud cover or atmospheric conditions (Figure 4d). Additionally, this cloud mask incorporates classes that denote different Earth’s surfaces, such as snow/ice, water, forests, and others, which can exhibit characteristics resembling clouds or cloud shadows [33,46]. A more intricate and comprehensive understanding of satellite imagery can be attained by adopting this approach.
3.1. Generic Cloud Detection Classification
- Manual Cloud Detection: It is the most reliable and highly accurate method of cloud detection used so far. This method requires a human expert to visually interpret the true-color and false-color composite of satellite imagery rather than mark the boundaries of different classes of cloud-cover areas by drawing polygons [42]. While this method is highly accurate, it is time-consuming, labor-intensive, and requires expertise [47]. This technique was viable during periods when satellite data access was constrained. With the availability of free large-scale satellite data, this method may prove to be infeasible. However, it is a good option for generating a validation dataset that can be used for performance analysis of automated methods and active learning methods.
- Automated cloud detection: These methods use algorithms, specific rules, and programs that can detect clouds without direct human intervention [26]. They are designed to be faster and more efficient compared to manual cloud detection. These methods explore spectral and spatial signatures and characteristics of clouds to differentiate them from Earth’s objects. These methods range from simple threshold-based techniques to advanced machine learning/ deep learning methods. They are designed to be scalable and can quickly handle large data volumes [40]. However, these methods have some limitations that researchers are exploring to overcome:
- These methods produce commission and omission errors [48], especially in complex scenes.
- These approaches might fail to work with certain atmospheric conditions and clouds.
- These algorithms might not be universally applicable solutions and require some additional parameter tuning.
- Active Learning: It is a specialized algorithm, often rooted in machine learning, that collaborates with a human expert to arrive at a definitive conclusion regarding uncertain and ambiguous cloud-cover regions [33]. Human feedback is essential, from training to refining the algorithm’s accuracy over time [41]. It is also termed a human-in-the-loop approach. This approach represents an effort to combine human expertise with machine learning capabilities. However, it necessitates careful supervision in most instances.
3.2. Sensor Band-Based Cloud Detection Classification
- Single-band Cloud detection: This approach relies solely on the spatial information from a single dominant spectral band sensitive to cloud properties, such as the blue or cirrus band [49,50]. However, such detection approaches are very rare, and their ability to distinguish clouds from other bright surfaces is limited, especially under complex atmospheric and surface conditions.
- Multi-band Cloud detection: The multi-band cloud detection approach capitalizes on a combination of captured multiple spectral bands to improve accuracy. This approach can be classified into four sub-categories:
- 3-band approach: This method employs the standard color bands (RGB) for cloud masking [51]. Since most satellites possess these three bands, it is a generic, versatile, and scalable approach applicable to a wide range of satellites.
- 4-band approach: This method uses the visible band (RGB) and NIR band to generate the cloud masks. These four bands have rich information required for cloud detection [52]. Most advanced remote sensing satellites like Landsat, Sentinel, PlanetScope, GeoFAN, IRS, etc., share these four bands, making this approach widely adaptable.
- All-band approach: These cloud detection methods are usually satellite-centric; they use all bands available with the satellite to generate cloud masks. Most threshold-based cloud detection uses this concept to generate cloud masks and perform atmospheric and geometric corrections.
4. Dataset Available
4.1. Baetens-Hagolle (CESBIO/CNES) Dataset
4.2. WHUS2-CD Dataset
4.3. KappaSet Dataset
4.4. IndiaS2 Dataset
5. Cloud Detection Methods
5.1. Threshold-Based Cloud Detection
5.1.1. Function of Mask (Fmask)
5.1.2. Sen2cor
5.1.3. MAJA
5.2. Machine Learning (ML) Cloud Detection
5.3. Deep Learning (DL) Cloud Detection
5.4. Importance of Multiclass Cloud Detection in Cloud Removal
6. Intercomparison Framework and Performance Evaluation
6.1. Data Harmonization and Input Consistency
6.2. Performance Evaluation Metrics
- For multiclass cloud detection, extended confusion matrices (Figure 5b–d) are used to evaluate the performance metrics (Table 10) for each class (c), where n is the number of pixels, C is the number of classes, and True Positive (TPc), False Positive (FPc), False Negative (FNc), True Negative (TNc) are estimated as per [150,181]. The accuracy metrics for multiclass cloud detection are computed either as macro-averaged to give equal importance to each class by reducing the resultant impact of the dominant class or micro-averaged, which aggregates the contributions of all classes (Table 10).
6.3. Conversion Framework for Multiclass Comparison
7. Research Gaps
- Threshold-based methods exhibit limited universality and scalability when applied to imagery from different locations with rich diversity. Thresholds are often static discrimination, which may only be suitable for specific regions and cloud types. Given the richness and diversity of remote sensing data, dynamic thresholds that can adapt to different conditions through expert knowledge systems are needed. Although thick cloud detection is generally efficient for most methods, improving the detection of thin clouds and cloud shadows remains a challenge. Also, realizing a comparative analysis for different approaches is important, as most algorithms generate cloud masks at different pixel resolutions. Fmask produces cloud masks at 20 m, Sen2Cor at 30 m, and MAJA at 240 m.
- ML methods automatically handle threshold selection requirements but are dependent on effective feature selection and extraction methods, as the use of only spectral features generally leads to lower accuracy. Their performance can be enhanced by incorporating spectral and spatial features by considering an appropriate combination of conventional methods that generate handcrafted texture features. However, conventional methods of feature extraction are time-consuming, and a mechanism is required to consider spatial features along with spectral features automatically.
- DL methods perform well by automatically generating low to high-level spectral and spatial features at various levels. However, they have high computation costs and encounter difficulties in considering large patch sizes containing rich information about large structures like clouds, shadows, and snow regions. They also face challenges in attaining discriminating features between clouds and bright areas like snow/ice, buildings, and river beds, as well as cloud shadows vs. dark areas like water bodies and terrain shadows.
- The extraction of cloud leaves zero value in the imagery, either filled by mosaicking from multitemporal data or pixel-wise reconstruction. However, mosaicking fails to consider radiometric variation and tends to overlook the spatial distribution of pixel intensity. The pixel-wise reconstruction shows improvement, but most existing cloud removal methods are unsuitable for addressing thin clouds as well as large areas covered by thick clouds. Also, an efficient automated multiclass cloud detection technique is required to consider thick vs. thin cloud separation. Most cloud removal methods consider true-color or false-color image composites, while each spectral band is affected by cloud presence; therefore, a mechanism to handle cloud in each spectral band is needed.
8. Conclusions
Funding
Conflicts of Interest
References
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Band Names | Landsat-7 | Landsat 8 | Sentinel-2 | |||
---|---|---|---|---|---|---|
Band Index (Resolution) | Wavelength (µm) | Band Index (Resolution) | Wavelength (µm) | Band Index (Resolution) | Wavelength (µm) | |
Coastal | - | - | Band 1 (30 m) | 0.435–0.451 | Band 1 (60 m) | 0.433–0.453 |
Blue | Band 1 (30 m) | 0.441–0.514 | Band 2 (30 m) | 0.452–0.512 | Band 2 (10 m) | 0.458–0.523 |
Green | Band 2(30 m) | 0.519–0.601 | Band 3 (30 m) | 0.533–0.590 | Band 3 (10 m) | 0.543–0.578 |
Red | Band 3 (30 m) | 0.631–0.692 | Band 4 (30 m) | 0.636–0.673 | Band 4 (10 m) | 0.650–0.680 |
Red Edge 1 | - | - | - | - | Band 5 (20 m) | 0.698–0.713 |
Red Edge 2 | - | - | - | - | Band 6 (20 m) | 0.733–0.748 |
Red Edge 3 | - | - | - | - | Band 7 (20 m) | 0.765–0.785 |
Wide NIR | - | - | - | - | Band 8 (10 m) | 0.785–0.900 |
Narrow NIR | Band 4 (30 m) | 0.772–0.898 | Band 5 (30 m) | 0.851–0.879 | Band 8A (20 m) | 0.855–0.875 |
Water Vapor | - | - | - | - | Band 9 (60 m) | 0.930–0.950 |
Cirrus | - | - | Band 9 (30 m) | 1.363–1.384 | Band 10 (60 m) | 1.365–1.385 |
SWIR1 | Band 5 (30 m) | 1.547–1.749 | Band 6 (30 m) | 1.566–1.651 | Band 11 (20 m) | 1.565–1.655 |
SWIR2 | Band 7 (30 m) | 2.064–2.345 | Band 7 (30 m) | 2.107–2.294 | Band 12 (20 m) | 2.100–2.280 |
Panchromatic | Band 8 (15 m) | 0.515–0.896 | Band 8 (15 m) | 0.503–0.676 | - | - |
TIR-1 | Band 6 (60 m) | 10.31–12.36 | Band 10 (100 m) | 10.60–11.19 | - | - |
TIR-2 | - | - | Band 11 (100 m) | 11.50–12.51 | - | - |
Location | Tile ID | Sentinel | Acquisition Date | Scene Information |
---|---|---|---|---|
Railroad Valley, USA | T11SPC | S2A | 5 January 2017 | Small cumulus over bright soil |
S2B | 27 August 2017 | Large cumulus over bright soil | ||
Alta Floresta, Brazil | T21LWK | S2A | 5 May 2018 | Scattered small cumulus |
S2B | 9 June 2018 | Thin cirrus | ||
Marrakech, Morocco | T29RPQ | S2A | 17 April 2016 | Scattered cumulus and thin cirrus |
S2A | 21 June 2017 | Clear image with snow and thin cirrus | ||
Arles, France | T31TFJ | S2A | 17 September 2017 | Large cloud cover |
S2B | 2 October 2017 | Thick and thin clouds | ||
Orleans, France | T31UDP | S2A | 16 May 2017 | Thick and thin cirrus clouds |
S2B | 19 August 2017 | Large mid-altitude cloud cover | ||
Ispra, Italy | T32TMR | S2A | 15 August 2017 | Clouds over mountains with snow |
S2B | 9 October 2017 | Clouds over mountains with snow and bright soil | ||
Gobabeb, Namibia | T33KWP | S2A | 21 December 2016 | Thick clouds above the desert |
S2B | 9 September 2017 | Small and low clouds | ||
Mongu, Zambia | T34LGJ | S2A | 12 November 2016 | Large thick cloud cover and some cirrus |
S2B | 4 August 2017 | Clear image and a few mid-altitude clouds | ||
Pretoria, South Africa | T35JPM | S2A | 13 March 2017 | Diverse cloud types |
S2A | 20 August 2017 | Scattered small clouds | ||
Railroad Valley, USA | T11SPC | S2B | 13 February 2018 | Large stratus and some cumulus |
Alta Floresta, Brazil | T21LWK | S2A | 14 July 2018 | Mid-altitude small clouds |
S2A | 13 August 2018 | Thin cirrus | ||
Marrakech, Morocco | T29RPQ | S2A | 18 December 2017 | Scattered cumulus and snow |
Arles, France | T31TFJ | S2B | 21 December 2017 | Mid-altitude thick clouds and snow |
Orleans, France | T31UDP | S2B | 18 February 2018 | Stratus cloud |
Ispra, Italy | T32TMR | S2B | 11 November 2017 | Clouds over mountains and mist |
Gobabeb, Namibia | T33KWP | S2B | 9 February 2018 | High and thin clouds |
Mongu, Zambia | T34LGJ | S2B | 13 October 2017 | Large thin cirrus cover |
Pretoria, South Africa | T35JPM | S2B | 13 December 2017 | Altostratus and small scattered clouds |
Munich, Germany | T32UPU | S2A | 22 April 2018 | Mostly cloud-free with a few small clouds |
S2B | 24 April 2018 | Large cloud cover with cumulus and cirrus |
Label | Class Name |
---|---|
0 | No Fill |
1 | No Data |
2 | Low Cloud |
3 | High Cloud |
4 | Cloud Shadow |
5 | Ground |
6 | Water |
7 | Snow |
Location | Tile ID | Acquisition Date | Scene Information |
---|---|---|---|
Yiwu, China | T46TFN | 14 July 2019 | Cloud over snow cover and barren |
Henan, China | T47SQU | 19 December 2019 | Cloud over barren having snow cover |
Washixia, China | T45SWC | 30 June 2019 | Scattered cloud over the mountain with snow |
Tibet | T46RGV | 15 December 2019 | Cloud over snow |
Tibet | T45SXR | 30 September 2018 | Cloud over barren and water |
Taila, China | T51TWM | 17 March 2020 | Cloud over ice, snow, and barren |
Bachu, China | T44TKK | 16 August 2018 | Cloud over barren and clear farmland |
Wentugaole, China | T47TQF | 23 October 2019 | Large cloud cover |
Shangyi, China | T50TKL | 24 August 2018 | Cloud over forest and farmland |
Yongding, China | T50RMN | 18 November 2019 | Cloud cover with forest and green area |
Songyang, China | T50RQS | 16 September 2019 | Large cloud cover over greenery |
Mengzhou, China | T49SFU | 19 August 2019 | Thin and thick clouds over urban |
Koldeneng, China | T44TPN | 15 August 2019 | Large cloud cover |
Qingyuan, China | T51TXG | 10 April 2020 | Scattered cloud over dryland |
Junhe, China | T50TQQ | 2 October 2019 | Scattered thick and thin cloud |
Luochuan, China | T49SCV | 29 April 2018 | A few small clouds over the forest |
Guyuan, China | T51UWS | 6 May 2020 | A few scattered clouds over barren |
Yanyuan, China | T47RQL | 25 March 2020 | Scattered clouds with barren and forest |
Rongjiang, China | T49RBJ | 28 September 2019 | Scattered clouds over the forest |
Dazhou, China | T48RYV | 27 August 2018 | A few small clouds over greenery and urban |
Yangchun, China | T49QEE | 22 February 2020 | A few scattered clouds over the forest |
Qianjiang, China | T49RFP | 22 July 2018 | A few scattered clouds over the forest with urban |
Pingxiang, China | T49RGL | 29 July 2018 | Clouds over the forest with urban |
Wulian, China | T50SPE | 6 May 2020 | Scattered thick and thin clouds over diverse region |
Zhanjiang, China | T49QDD | 30 September 2018 | Scattered small clouds over the coastal region |
Changzhou & Wuxi, China | T51STR | 5 November 2019 | Scattered small clouds over shrubland |
Linshui, China | T48RXU | 12 August 2019 | Scattered small thick clouds |
Yilan, China | T52TES | 2 June 2019 | Thick cloud over Barren and Forest |
Baotou, China | T49TCF | 28 March 2019 | Thick clouds over Barren |
Altay, China | T45TXN | 2 October 2019 | Scattered cloud with snow cover |
Tibet | T46SFC | 16 April 2020 | Cloud over snow and bright mountain |
Tibet | T44SPC | 28 May 2020 | Cloud over the mountain with small snow cover |
Label | Class Name |
---|---|
0 | Undefined (Labeler not sure) |
1 | Clear |
2 | Cloud Shadow |
3 | Semi-transparent Cloud |
4 | Cloud |
5 | Missing (No Data or Fill) |
S.No | Month | Total Tiles | Total Imagery | Total Sub-Tiles | |
---|---|---|---|---|---|
Train | 1 | January | 60 | 60 | 290 |
2 | February | 88 | 88 | 476 | |
3 | March | 84 | 86 | 698 | |
4 | April | 72 | 73 | 527 | |
5 | May | 121 | 126 | 2271 | |
6 | June | 115 | 117 | 745 | |
7 | July | 95 | 99 | 1172 | |
8 | August | 89 | 90 | 1066 | |
9 | September | 64 | 64 | 273 | |
10 | October | 87 | 88 | 556 | |
11 | November | 61 | 61 | 358 | |
12 | December | 3 | 3 | 16 | |
Test | 13 | All Months | 119 | 124 | 803 |
Location | Tile Id | Acquisition Date | Scene Description | |
---|---|---|---|---|
Train | Bhavnagar, Gujrat | T42QZJ | 28 March 2022 | Clear coastal area with urban bright objects |
Jodhpur, Rajasthan | T43RCK | 16 June 2022 | Thick clouds with bright urban and desert objects | |
Hanumangarh, Rajasthan | T43RDN | 26 June 2022 | Mostly clear desert and urban area | |
Srinagar, Jammu & Kashmir | T43SDT | 4 October 2022 | Thick and thin clouds over the mountain with snow patches | |
Srikakulam, Andhra Pradesh | T44QRF | 28 November 2022 | Sparsely cloudy sea with clear coastal area | |
Tinsukia, Assam | T46RGR | 29 November 2022 | Thick and thin cloud over forest and clear dried riverbed | |
Test | Thiruvananthapuram, Kerala | T43PFK | 16 August 2022 | Thick and thin clouds scattered over land and sea. |
Bathinda, Punjab | T43RDP | 23 November 2022 | Thin cloud over cultivated farmland | |
Shimla, Himachal Pradesh | T43RFQ | 20 November 2022 | Thick clouds over mountain with little snow | |
Pithora, Chhattisgarh | T44QPJ | 21 December 2022 | Sparsely distributed near-to-invisible thin clouds | |
Haldwani, Uttarakhand | T44RLT | 28 October 2022 | Thick clouds over forest and mountain ranges |
Label | Class Name |
---|---|
0 | No Fill |
1 | No data |
2 | Thick Cloud |
3 | Thin Cloud |
4 | Cloud Shadow |
5 | Ground |
S. No | Author (Year) | Paper Title | Technique(s) | Satellite Sensor/Instrument (Dataset(s)) | Cloud Mask Type | Evaluation Parameter | Main Highlights |
---|---|---|---|---|---|---|---|
1. | Bai et al. (2016) [71] | “Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion” | SVM-RBF (GLCM + NDVI) |
| Binary |
|
|
2. | Tan et al. (2016) [72] | “Cloud extraction from Chinese high-resolution satellite imagery by probabilistic latent semantic analysis and object-based machine learning” | SVM (PLSA+SLIC) |
| Binary |
|
|
3. | Shao et al. (2017) [73] | “Fuzzy AutoEncode Based Cloud Detection for RemoteSensing Imagery” | Fuzzy Autoencode Model (FAEM) |
| Binary |
|
|
4. | Perez-Suay et al. (2017) [74] | “Randomized kernels for large scale Earth observation applications” | Randomized Kernels |
| Binary |
|
|
5. | Sun et al. (2018) [75] | “SVM-Based Cloud Detection Using Combined Texture Features” | SVM (GLCM + RIULBP) |
| Binary |
|
|
6. | Ishida et al. (2018) [76] | “Development of a support vector machine-based cloud detection method for MODIS with the adjustability to various conditions” | SVM |
| Binary |
|
|
7. | P’erez-Suay et al. (2018) [77] | “Pattern recognition scheme for large-scale cloud detection over landmarks” | SVM |
| Binary |
|
|
8. | Deng et al. (2018) [78] | “Cloud detection in satellite images based on natural scene statistics and Gabor features” | SVM (Gabor) |
| Binary |
|
|
9. | Ghasemian and Akhoondzadeh (2018) [79] | “Introducing two Random Forest based methods for cloud detection in remote sensing images” | RF |
| Multi-class(Thick, Thin cloud, snow/ice and background) |
|
|
10. | Fu et al. (2018) [80] | “Cloud detection for FY meteorology satellite based on ensemble thresholds and random forests approach” | RF |
| Binary |
|
|
11. | Joshi et al. (2019) [81] | “Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8” | SVM |
| Multiclass (cloud, shadow, and ground) |
|
|
12. | Chen et al. (2020) [82] | “A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images” | Classification Extension-based Cloud Detection (CECD) using RF |
| Binary |
|
|
13. | Cilli et al. (2020) [83] | “Machine learning for cloud detection of globally distributed Sentinel-2 images” | RFSVMMLP |
| Binary |
|
|
14. | Wei et al. (2020) [84] | “Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches” | RFmask (using RF) |
| Binary |
|
|
15. | Ibrahim et al. (2021) [85] | “Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery” | SVM |
| Multiclass (cloud, cloud shadow, cirrus, and clear) |
|
|
16. | Li et al. (2022) [86] | “An automatic cloud detection model for Sentinel-2 imagery based on Google Earth Engine” | SVM |
| Binary |
|
|
17. | Yao et al. (2022) [87] | “Optical remote sensing cloud detection based on random forest only using the visible light and near-infrared image bands” | RFCD (Random Forest Cloud Detection) |
| Binary |
|
|
18. | Singh et al. (2023) [88] | “Cloud detection using sentinel 2 images: a comparison of XGBoost, RF, SVM, and CNN algorithms” | XGBoostRFSVM(each using combination of spectral (S) +GLCM (G) + Morphological (M) + Bilateral (B), and ResNet14) |
| 6-class Binary |
|
|
19. | Singh et al. (2023) [89] | “An Automated Cloud Detection Method for Sentinel-2 Images” | XGBoostRFSVM(each using a pixel-wise patch-based mechanism) |
| 6-class |
|
|
20. | Shang et al. (2024) [90] | “A hybrid cloud detection and cloud phase classification algorithm using classic threshold-based tests and extra randomized tree model” | Threshold and Extra Randomized Tree (CARE algorithm) |
| Multiclass (cloud, probably cloud, clear, and probably clear) |
|
|
Deep Learning-based Cloud Detection: | |||||||
21. | Jeppesen et al. (2019) [101] | “A cloud detection algorithm for satellite imagery based on deep learning” | Remote Sensing Network (RS-NET based on U-Net) |
| Binary |
|
|
22. | Xu et al. (2019) [102] | “DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network” | Deepmask (based on ResNet) |
| Binary |
|
|
23. | Yang et al. (2019) [103] | “CDnet: CNN-based cloud detection for remote sensing imagery” | Cloud Detection Network (CD-NET based on CNN) |
| Binary |
|
|
24. | Shendryk et al. (2019) [104] | “Deep learning for multi-modal classification of cloud, shadow, and land cover scenes in PlanetScope and Sentinel-2 imagery” | Ensembled DenseNet201, ResNet50 and VGG10 |
| Multiclass (Clear, Partly cloudy, Cloudy, Haze) |
|
|
25. | Liu et al. (2019) [105] | “Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation” | CloudNet (deep residual network) |
| Binary |
|
|
26. | Kanu et al. (2020) [106] | “CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images” | CloudX-net (CNN) |
| Binary |
|
|
27. | Segal-Rozenhaimer et al. (2020) [107] | “Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN)” | Deeplab (CNN based cloud and cloud shadow detection) |
| Binary |
|
|
28. | Kristollari et al. (2020) [111] | “Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery” | Patch-to-pixel CNN architecture |
| 6-class |
|
|
29. | Luotamo et al. (2021) [112] | “Multiscale Cloud Detection in Remote Sensing Images Using a Dual Convolutional Neural Network” | Two-cascaded CNN architecture |
| Binary |
|
|
30. | Ma et al. (2021) [113] | “Cloud detection algorithm for multi-satellite remote sensing imagery based on a spectral library and 1D convolutional neural network” | Cloud detection based on CNN using spectral library (CD-SLCNN based on 1D Residual Network) |
| Binary |
|
|
31. | Lopez-Puigdollers et al. 2021 [118] | “Benchmarking Deep Learning Models for Cloud Detection in Landsat-8 and Sentinel-2 Images” | Fully convolutional neural networks (FCNN) based on UNet |
| Binary |
|
|
32. | Li et al. 2021 [56] | “A Lightweight Deep Learning-Based Cloud Detection Method for Sentinel-2A Imagery Fusing Multiscale Spectral and Spatial Features” | Cloud Detection-fusing multiscale spectral and spatial features (CD-FM3SFs) |
| Binary |
|
|
33. | Li et al. (2022) [124] | “A hybrid generative adversarial network for weakly-supervised cloud detection in multispectral images” | GAN-CDM |
| Binary |
|
|
34. | Grabowski et al. (2023) [143] | “Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection” | nnU-Nets |
| Binary4-class |
|
|
35. | Zhang et al. (2023) [149] | “CloudViT: A Lightweight Vision TransformerNetwork for Remote Sensing Cloud Detection” | CloudViT |
| Binary |
|
|
36. | Singh et al. (2023) [150] | “A transformer-based cloud detection approach using Sentinel 2 images” | SSATR-CD (Spatial-spectral Attention Transformer using Cloud Detection) |
| 4-classBinary |
|
|
37. | Francis (2024) [151] | “Sensor Independent Cloud and Shadow Masking with Partial Labels and Multimodal Inputs” | SegFormer |
| Binary |
|
|
38. | Singh et al. (2024) [156] | “Enhanced cloud detection in Sentinel-2 imagery using K-means clustering embedded transformer-inspired models” | KET-CD (Kmeans embedded Transformer inspired methods) |
| 4-class |
|
|
39. | Wright et al. (2024) [158] | “CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery” | CloudS2Mask based on UNet |
| 4-class |
|
|
40. | Gbodjp et al. (2025) [159] | “Self-supervised representation learning for cloud detection using Sentinel-2 images” | DeepCluster |
| Binary |
|
|
Metrics | Computation Formula | ||
---|---|---|---|
Binary | Multiclass | ||
Macro-Averaged | Micro-Averaged | ||
Accuracy | |||
F1-score | |||
Precision | |||
Recall | |||
Kappa coefficient | |||
Where, and | |||
mIoU | |||
User Accuracy (UA) | |||
Producer Accuracy (PA) |
Binary | 4-Class | 6-Class | Fmask | Sen2Cor | |||||
---|---|---|---|---|---|---|---|---|---|
Value | Class | Value | Class | Value | Class | Value | Class | Value | Class |
0 | Cloud | 2 | Thick cloud | 2 | Low cloud | 4 | Cloud | 9 | Cloud high probability |
3 | Thin cloud | 3 | High cloud | - | - | 8 | Cloud medium probability | ||
10 | Thin cirrus | ||||||||
1 | Clear (non-cloud) | 4 | Cloud shadow | 4 | Cloud shadow | 2 | Cloud shadow | 3 | Cloud shadow |
5 | Ground | 5 | Ground | 0 | Clear land | 2 | Dark area pixels | ||
4 | Vegetation | ||||||||
5 | Bare soil | ||||||||
7 | Unclassified | ||||||||
6 | Water | 1 | Water | 6 | Water | ||||
7 | Snow/ice | 3 | Snow | 11 | Snow |
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Singh, R.; Pal, M.; Biswas, M. Cloud Detection Methods for Optical Satellite Imagery: A Comprehensive Review. Geomatics 2025, 5, 27. https://doi.org/10.3390/geomatics5030027
Singh R, Pal M, Biswas M. Cloud Detection Methods for Optical Satellite Imagery: A Comprehensive Review. Geomatics. 2025; 5(3):27. https://doi.org/10.3390/geomatics5030027
Chicago/Turabian StyleSingh, Rohit, Mahesh Pal, and Mantosh Biswas. 2025. "Cloud Detection Methods for Optical Satellite Imagery: A Comprehensive Review" Geomatics 5, no. 3: 27. https://doi.org/10.3390/geomatics5030027
APA StyleSingh, R., Pal, M., & Biswas, M. (2025). Cloud Detection Methods for Optical Satellite Imagery: A Comprehensive Review. Geomatics, 5(3), 27. https://doi.org/10.3390/geomatics5030027