Improved cloud detection in GOES scenes over the oceans

Moderate resolution imaging spectrometer (MODIS) images are widely used in land, ocean, and atmospheric monitoring, due to their wide spectral coverage, high temporal resolution, and convenient data acquisition. Accurate cloud detection is critical to the fine processing and application of MODIS images. Owing to spatial resolution limitations and the influence of mixed pixels, most MODIS cloud detection algorithms struggle to effectively recognize of clouds and ground objects. Here, we propose a novel cloud detection method based on land surface reflectance and a multiscale feature convolutional neural network to achieve high-precision cloud detection, particularly for thin clouds and clouds over bright surface. A monthly surface reflectance dataset was constructed by MODIS products (MOD09A1) and employed to provide background information for cloud detection. Difference-based samples were obtained using surface reflectance as well MODIS images of different phases based on difference operations. The multiscale feature network (MFCD-Net) using an atrous spatial pyramid pooling and a channel and spatial attention module integrated low-level spatial features and high-level semantic information to capture multiscale features and generate a high-precision cloud mask. For cloud detection experiments and quantitative analysis, 61 MODIS images acquired at different times on various underlying surface types were used. Cloud detection results were compared to those of UNet, Deeplabv3+, UNet++, PSPNet, and top of atmosphere-based (MFCD-TOA) methods. The proposed method performed well, with the highest overall accuracy (96.55%), precision (92.13%), and recall (88.90%). It improved cloud detection accuracy in various scenarios, reducing thin cloud omission and bright surface misidentification.

Accurate cloud detection is a requirement of many geophysical applications that use visible and infrared satellite data (e.g. cloud climatologies, multichannel sea surface temperature (MCSST)). Unfortunately, a significant source of residual error in such satellite-based products is undetected cloud. Here, a new, computationally efficient cloud detection procedure for both daytime and night-time Advanced Very High Resolution Radiometer (AVHRR) data is developed. It differs substantially from our prior related work. First, a new clustering procedure is used, which produces more homogeneous and distinct clusters than those produced by either our previous work or the ISODATA algorithm of Ball and Hall. Second, the input information vector is reduced in size, incorporates both radiance and spatial components and each component is normalized. These changes improve the clustering/subsequent classification, tend to decrease execution time, and simplify post-processing of the classified (cloud, clear ocean) data to remove any residual outliers. Third, the enhanced performance makes possible the use of a multipass procedure which is very effective in identifying the complex multilayer cloud structures common in satellite data. Validation with independent lidar observations confirms the accuracy of the new procedure. Marine low stratiform clouds (LSCs-fog, stratus and stratocumulus) are also detected effectively. This advance is important because LSCs are a major source of residual cloud contamination in contemporary sea surface temperature (SST) products. Finally, the method is sufficiently general that it can be adapted to other sensors (e.g. the Along-Track Scanning Radiometer (ATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS)).

In this paper, an approach for automatic cloud detection and localization in satellite remote sensing images is introduced. Cloud detection is useful in improving the accuracy of land cover classification in cloudy satellite images. The accurate detection of clouds in satellite images is vital for many atmospheric and terrestrial applications. In this paper we propose an algorithm for automatic cloud detection based on neutrosophic set and wavelet transform. The proposed approach uses both color and texture features for cloud detection. The input image is transformed into Lab color model for extracting the color features and gray scale image for extracting the texture features. Transformed images are converted into neutrosophic domain. An indeterminacy reduction operation is performed for getting better results. Finally a Fuzzy C-means clustering is performed on the true subsets. This gives the cloud detected image. This method is efficient in detecting thick clouds and thin clouds in Landsat images. Result analysis shows that the proposed algorithm can effectively detect the thin cloud. The proposed algorithm gives accurate results in less time complexity.

We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along-Track Scanning Radiometer (AATSR) instrument and compare these to the standard threshold-based SADIST cloud detection scheme. We use a manually classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land-surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8–9% over SADIST (maximum score of 77.93% compared with 69.27%). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2 K tolerance over a 3 × 3 pixel domain. We find an increase of 5–7% in the observations falling within this tolerance when using Bayesian methods (maximum of 92.02% compared with 85.69%). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to be provided by the Sea and Land Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014).

Accurate cloud detection is a crucial step in analyzing optical satellite imagery.The increasing application of small satellites equipped only with Visible and Near-Infrared (VNIR) sensors necessitates the evaluation of cloud detection under limited spectral bands.This study focuses on the intercomparison of three cloud detection methodologies-a custom threshold-based method, a machine learning model (Extreme Gradient Boosting; XGBoost), and a deep learning model (U-Net)-using only five VNIR bands from the CloudSEN12+ dataset.As a result, the U-Net model demonstrated higher performance than other approaches, achieving an Overall Accuracy (OA) of 85.6% and an Intersection over Union (IoU) of 65.3%.In particular, the U-Net significantly outperformed others in detecting cloud shadows, achieving an accuracy of 77.4%, nearly double that of XGBoost (46.0%).In spatial analysis, the threshold method misclassified bright surfaces as clouds, and XGBoost generated salt-and-pepper noise at the pixel level.The U-Net accurately detected the complex boundaries of clouds and shadows.Our results indicate that deep learning approaches that leverage spatial information are highly effective for cloud detection with limited spectral bands.This study provides a quantitative baseline to inform the development of operational cloud-detection algorithms for VNIR-only satellite missions.

Accurate cloud detection is critical for advancing atmospheric monitoring and meteorological forecasting. This paper presents the Cloud Detection Challenge, an initiative aimed at enhancing cloud detection through innovative solutions using lidar-based ceilometer data. This initiative was hosted by IEEE MetroXRAINE 2024, and 11 teams participated in this initiative. Participants were provided with a novel dataset of backscatter profiles converted into time-height plots, offering unique insights into atmospheric conditions beyond conventional imagery. Data collection employed a Lufft CHM 15k ceilometer, capturing cloud dynamics every 15 seconds located near Mt. Etna, an active volcano in Italy. The dataset includes 1568 hourly labeled backscatter profiles, serving as a benchmark for state-of-the-art deep learning models. The challenge sets a baseline performance of 89.57% accuracy, 92.73% F1-score, 89.82% precision, and 95.84% recall, inviting participants to develop models to exceed these results. Submissions proposed a wide-range of AI-based approaches, including Transformer and Convolutional Neural Network architectures, showcasing the potential of advanced image analysis techniques in lidar-based cloud detection. This paper details the challenge framework, as well as the methodologies proposed by top-performing teams, offering a comparative evaluation of their effectiveness. Our initiative advances cloud detection technologies and underscores their broader implications for environmental monitoring, agriculture, and satellite imaging. The insights and dataset presented herein lay the groundwork for future advancements in leveraging lidar data for atmospheric analysis.

Cloud masking is an essential process for retrieving the sea surface temperature (SST) from infrared data observed by satellites. Many cloud masking algorithms have been proposed, but cloud contamination still remains as a difficult issue. One of the causes that make cloud masking difficult is a trade-off between the accuracy and the availability of retrieved SST. Hence, it is beneficial to understand this to improve cloud masking. We investigated the Second-generation Global Imager (SGLI) data for their sensitivity to clouds and the trade-offs that originate from using cloud masking. This paper discusses results of the investigation performed for the global ocean and those focusing on the Arctic Ocean from June to September

The deep convolutional neural network has made significant progress in cloud detection. However, the compromise between having a compact model and high accuracy has always been a challenging task in cloud detection for large-scale remote sensing imagery. A promising method to tackle this problem is knowledge distillation, which usually lets the compact model mimic the cumbersome model’s output to get better generalization. However, vanilla knowledge distillation methods cannot properly distill the characteristics of clouds in remote sensing images. In this paper, we propose a novel self-attention knowledge distillation approach for compact and accurate cloud detection, named Bidirectional Self-Attention Distillation (Bi-SAD). Bi-SAD lets a model learn from itself without adding additional parameters or supervision. With bidirectional layer-wise features learning, the model can get a better representation of the cloud’s textural information and semantic information, so that the cloud’s boundaries become more detailed and the predictions become more reliable. Experiments on a dataset acquired by GaoFen-1 satellite show that our Bi-SAD has a great balance between compactness and accuracy, and outperforms vanilla distillation methods. Compared with state-of-the-art cloud detection models, the parameter size and FLOPs are reduced by 100 times and 400 times, respectively, with a small drop in accuracy.

Accurate cloud detection is a requirement of many geophysical applications that use visible and infrared satellite data (e.g. cloud climatologies, multichannel sea surface temperature (MCSST)). Unfortunately, a significant source of residual error in such satellite-based products is undetected cloud. Here, a new, computationally efficient cloud detection procedure for both daytime and night-time Advanced Very High Resolution Radiometer (AVHRR) data is developed. It differs substantially from our prior related work. First, a new clustering procedure is used, which produces more homogeneous and distinct clusters than those produced by either our previous work or the ISODATA algorithm of Ball and Hall. Second, the input information vector is reduced in size, incorporates both radiance and spatial components and each component is normalized. These changes improve the clustering/subsequent classification, tend to decrease execution time, and simplify post-processing of the classified (cloud, clear ocean) data ...

Comparison of cloud detection algorithms for Sentinel-2 imagery

Cloud contamination is a critical source of errors in the data assimilation of hyperspectral infrared radiance (IR). Therefore, it is necessary to filter out cloudy observations. In this study, we review and summarize the principles and research progress of cloud detection methods for the hyperspectral IR in the past two decades. Based on the impact of IR data utilization on cloud detection results, cloud detection methods are categorized into five types, namely clear field-of-view (FOV) detection, clear channel detection, three-dimensional cloud detection, cloud-clearing and deep learning methods. Clear FOV methods and clear channel methods aim to identify the purely clear FOVs and spectral channels that are not affected by clouds, respectively. Cloud-clearing methods are used to reconstruct clear-column radiance for cloudy observations. Deep learning cloud detection methods can quickly learn the mapping relationship between infrared hyperspectral radiation characteristics and FOV cloud distribution from a large amount of infrared radiative information with known FOV cloud labels. In this paper, we discuss and provide an outlook on the key issues in current hyperspectral IR cloud detection. Specifically, we analyze and summarize the factors affecting cloud detection, such as surface background information, vertical cloud distribution, hyperspectral IR channel selection, improvements in cloud detection algorithms and model applicability. The results indicate the use of deep learning methods offer advantages in detection accuracy and algorithm efficiency of hyperspectral IR cloud detection.

Analysis of along track scanning radiometer-2 (ATSR-2) data for clouds, glint and sea surface temperature using neural networks

The existence of clouds has seriously affected the application of remote sensing data. Therefore, accurate cloud detection is of great significance in remote sensing image processing and application. Traditional cloud detection methods are complex to operate and often require the additional ancillary information. An automatic cloud detection method based on convolutional neural network (CNN) is proposed in this study. The method utilizes a convolutional network structure to classify training samples for cloud and non-cloud. In order to make full use of image information, images of different band numbers are applied to evaluate the influence of the spectrum on cloud detection. Experiments and verification on Landsat 8 images show that the proposed method based on CNN can comprehensively and automatically detect different types of clouds on different surface types, and the cloud detection result using 7 bands is the optimal. The algorithm takes full advantage of image information and does not rely on thermal infrared information, which has practical application value for improving image utilization and subsequent retrieval of remote sensing parameters.

Clouds are an important component of weather and climate change and have a significant impact on ecology, weather forecasting and aviation safety. Cloud detection is a preprocessing step in many satellite image processing and remote sensing inversion. Clouds not only cover the subsurface, but also absorb and scatter solar radiation, which affects the remote sensing analysis and study of the surface and reduces the utilization of data. Accurate detection of cloud-covered areas in satellite images can effectively reduce the interference of cloud cover on remote sensing data application. The Fengyun-4B Satellite is the latest generation of geostationary meteorological satellites in China, with significant performance advantages. In this paper, a land cloud detection algorithm for AGRI sensors is constructed based on the existing cloud detection algorithm using the AGRI full disk data of Fengyun 4B Satellite. Through the analysis of cloud detection product quality, the algorithm threshold is optimized to significantly improve the accuracy of cloud detection. The experimental results show that the algorithm has good overall cloud detection performance in full disk imagery, especially in the thick cloud region, but there are some misclassifications in the high brightness surface region such as the Tibetan Plateau. This study expands the application scenarios of domestic geostationary meteorological satellites, and provides new technical support for meteorological monitoring and remote sensing applications.

Clouds play a major role in controlling Earth's climate, and cloud detection is a crucial step in the numerical weather prediction, and global climate models. Multi-angle imaging spectroradiometer (MLSR) and moderate resolution imaging spectroradiometer (MODIS) were launched in 1999 by clouds. However, cloud detection algorithms using either MISR or MODIS data separately do not take full advantage of the data collected by both sensors. In this paper, we propose and test two schemes to combine MISR and MODIS data for cloud detection in polar regions, Both schemes are follow-ups of a two-step polar cloud detection algorithm using MISR data: enhanced linear correlation matching classification followed by quadratic discriminate analysis (ELMS-QDA) (T, Shi et al.). The first scheme is mapping the MODIS cloud detection results to the MISR grid based on a nearest neighbor method, then only reporting the agreed pixels of the ELCMC-QDA results (from MISR) and MODIS operational results. This scheme improves the classification accuracy, but reduces the coverage of the results. Instead of combining the MISR and MODIS results directly, the second scheme uses the agreed pixels of ELCMC results and MODIS operational results as the training data for the QDA on MISR features, and output the results from the QDA. Both schemes are tested over a region where expert labels show that both MISR and MODIS operational algorithms do not work well (according to expert labels, 53% and 12.72% misclassification rates for MISR and MODIS operational algorithms respectively). The first scheme only makes 0.72% of errors, but leaves 68.72% of pixels unclassified. The second scheme reaches a 2.93% of misclassification rate, which is smaller than a 4.09% rate from ELCMC-QDA, and it provides a full coverage. Hence we propose using QDA on ELCMC and MODIS agreed pixels as an algorithm to fuse the MISR and MODIS information for the polar cloud detection.