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1
Van Tricht, K., I. V. Gorodetskaya, S. Lhermitte, D. D. Turner, J. H. Schween, and N. P. M. Van Lipzig. "An improved algorithm for cloud base detection by ceilometer over the ice sheets." Atmospheric Measurement Techniques Discussions 6, no. 6 (November 14, 2013): 9819–55. http://dx.doi.org/10.5194/amtd-6-9819-2013.
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Abstract. Optically thin ice clouds play an important role in polar regions due to their effect on cloud radiative impact and precipitation on the surface. Cloud bases can be detected by lidar-based ceilometers that run continuously and therefore have the potential to provide basic cloud statistics including cloud frequency, base height and vertical structure. Despite their importance, thin clouds are however not well detected by the standard cloud base detection algorithm of most ceilometers operational at Arctic and Antarctic stations. This paper presents the Polar Threshold (PT) algorithm that was developed to detect optically thin hydrometeor layers (optical depth τ ≥ 0.01). The PT algorithm detects the first hydrometeor layer in a vertical attenuated backscatter profile exceeding a predefined threshold in combination with noise reduction and averaging procedures. The optimal backscatter threshold of 3 × 10−4 km−1 sr−1 for cloud base detection was objectively derived based on a sensitivity analysis using data from Princess Elisabeth, Antarctica and Summit, Greenland. The algorithm defines cloudy conditions as any atmospheric profile containing a hydrometeor layer at least 50 m thick. A comparison with relative humidity measurements from radiosondes at Summit illustrates the algorithm's ability to significantly differentiate between clear sky and cloudy conditions. Analysis of the cloud statistics derived from the PT algorithm indicates a year-round monthly mean cloud cover fraction of 72% at Summit without a seasonal cycle. The occurrence of optically thick layers, indicating the presence of supercooled liquid, shows a seasonal cycle at Summit with a monthly mean summer peak of 40%. The monthly mean cloud occurrence frequency in summer at Princess Elisabeth is 47%, which reduces to 14% for supercooled liquid cloud layers. Our analyses furthermore illustrate the importance of optically thin hydrometeor layers located near the surface for both sites, with 87% of all detections below 500 m for Summit and 80% below 2 km for Princess Elisabeth. These results have implications for using satellite-based remotely sensed cloud observations, like CloudSat, that may be insensitive for hydrometeors near the surface. The results of this study highlight the potential of the PT algorithm to extract information in polar regions about a wide range of hydrometeor types from measurements by the robust and relatively low-cost ceilometer instrument.
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Van Tricht, K., I. V. Gorodetskaya, S. Lhermitte, D. D. Turner, J. H. Schween, and N. P. M. Van Lipzig. "An improved algorithm for polar cloud-base detection by ceilometer over the ice sheets." Atmospheric Measurement Techniques 7, no. 5 (May 6, 2014): 1153–67. http://dx.doi.org/10.5194/amt-7-1153-2014.
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Abstract. Optically thin ice and mixed-phase clouds play an important role in polar regions due to their effect on cloud radiative impact and precipitation. Cloud-base heights can be detected by ceilometers, low-power backscatter lidars that run continuously and therefore have the potential to provide basic cloud statistics including cloud frequency, base height and vertical structure. The standard cloud-base detection algorithms of ceilometers are designed to detect optically thick liquid-containing clouds, while the detection of thin ice clouds requires an alternative approach. This paper presents the polar threshold (PT) algorithm that was developed to be sensitive to optically thin hydrometeor layers (minimum optical depth τ ≥ 0.01). The PT algorithm detects the first hydrometeor layer in a vertical attenuated backscatter profile exceeding a predefined threshold in combination with noise reduction and averaging procedures. The optimal backscatter threshold of 3 × 10−4 km−1 sr−1 for cloud-base detection near the surface was derived based on a sensitivity analysis using data from Princess Elisabeth, Antarctica and Summit, Greenland. At higher altitudes where the average noise level is higher than the backscatter threshold, the PT algorithm becomes signal-to-noise ratio driven. The algorithm defines cloudy conditions as any atmospheric profile containing a hydrometeor layer at least 90 m thick. A comparison with relative humidity measurements from radiosondes at Summit illustrates the algorithm's ability to significantly discriminate between clear-sky and cloudy conditions. Analysis of the cloud statistics derived from the PT algorithm indicates a year-round monthly mean cloud cover fraction of 72% (±10%) at Summit without a seasonal cycle. The occurrence of optically thick layers, indicating the presence of supercooled liquid water droplets, shows a seasonal cycle at Summit with a monthly mean summer peak of 40 % (±4%). The monthly mean cloud occurrence frequency in summer at Princess Elisabeth is 46% (±5%), which reduces to 12% (±2.5%) for supercooled liquid cloud layers. Our analyses furthermore illustrate the importance of optically thin hydrometeor layers located near the surface for both sites, with 87% of all detections below 500 m for Summit and 80% below 2 km for Princess Elisabeth. These results have implications for using satellite-based remotely sensed cloud observations, like CloudSat that may be insensitive for hydrometeors near the surface. The decrease of sensitivity with height, which is an inherent limitation of the ceilometer, does not have a significant impact on our results. This study highlights the potential of the PT algorithm to extract information in polar regions from various hydrometeor layers using measurements by the robust and relatively low-cost ceilometer instrument.
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Li, Xiaolong, Hong Zheng, Chuanzhao Han, Wentao Zheng, Hao Chen, Ying Jing, and Kaihan Dong. "SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images." Remote Sensing 13, no. 15 (July 24, 2021): 2910. http://dx.doi.org/10.3390/rs13152910.
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Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.
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Stubenrauch, C. J., S. Cros, A. Guignard, and N. Lamquin. "A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat." Atmospheric Chemistry and Physics Discussions 10, no. 3 (March 30, 2010): 8247–96. http://dx.doi.org/10.5194/acpd-10-8247-2010.
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Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height as well as to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated as about 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover (0 or 0.3). 40% of all clouds are high clouds, and about 44% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thickness optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.
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Stubenrauch, C. J., S. Cros, A. Guignard, and N. Lamquin. "A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat." Atmospheric Chemistry and Physics 10, no. 15 (August 6, 2010): 7197–214. http://dx.doi.org/10.5194/acp-10-7197-2010.
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Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height. In addition, they permit to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated from 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover from 0 to 0.3. 42% of all clouds are high clouds, and about 42% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thickness optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.
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Li, Zhi, Haitao Xu, and Yanzhu Liu. "A differential game model of intrusion detection system in cloud computing." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668799. http://dx.doi.org/10.1177/1550147716687995.
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The multi-mesh distributed and open structure of cloud computing is more weak and vulnerable to security threats. Intrusion detection system should be incorporated in cloud infrastructure to monitor cloud resources against security attacks. In this article, the interaction between rational cloud resource defender and the potential malicious user in the cloud as a differential game is investigated. The feedback Nash equilibrium of the game is reviewed and a complex decision-making process and interactions between the cloud resource defender and a malicious user of cloud are also analyzed. The system results support a theoretical foundation in detecting the malicious attack, which can help cloud intrusion detection system make the optimal dynamic strategies to improve the defensive ability.
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Liu, Lei, Xuejin Sun, Feng Chen, Shijun Zhao, and Taichang Gao. "Cloud Classification Based on Structure Features of Infrared Images." Journal of Atmospheric and Oceanic Technology 28, no. 3 (March 1, 2011): 410–17. http://dx.doi.org/10.1175/2010jtecha1385.1.
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Abstract Some cloud structure features that can be extracted from infrared images of the sky are suggested for cloud classification. Both the features and the classifier are developed over zenithal images taken by the whole-sky infrared cloud-measuring system (WSIRCMS), which is placed in Nanjing, China. Before feature extraction, the original infrared image was smoothed to suppress noise. Then, the image was enhanced using top-hat transformation and a high-pass filtering. Edges are detected from the enhanced image after adaptive optimization threshold segmentation and morphological edge detection. Several structural features are extracted from the segment image and edge image, such as cloud gray mean value (ME), cloud fraction (ECF), edge sharpness (ES), and cloud mass and gap distribution parameters, including very small-sized cloud mass and gaps (SMG), middle-sized cloud gaps (MG), medium–small-sized cloud gaps (MSG), and main cloud mass (MM). It is found that these features are useful for distinguishing cirriform, cumuliform, and waveform clouds. A simple but efficient supervised classifier called the rectangle method is used to do cloud classification. The performance of the classifier is assessed with an a priori classification carried out by visual inspection of 277 images. The index of agreement is 90.97%.
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Tinkham, Wade T., and Neal C. Swayze. "Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models." Forests 12, no. 2 (February 22, 2021): 250. http://dx.doi.org/10.3390/f12020250.
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Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.
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Li, Zongmin, Chunchun Yao, Yujie Liu, and Hua Li. "Vehicle Detection Based on Structure Perception in Point Cloud." Journal of Computer-Aided Design & Computer Graphics 33, no. 3 (March 1, 2021): 405–12. http://dx.doi.org/10.3724/sp.j.1089.2021.18368.
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Hatipoğlu, P. U., R. T. Albayrak, and A. A. Alatan. "OBJECT DETECTION UNDER MOVING CLOUD SHADOWS IN WAMI." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 837–44. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-837-2020.
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Abstract. For a reliable and robust moving object detection, the subtraction of a precisely modeled background is crucial in wide-area motion imagery (WAMI). Even the most successful background subtraction algorithms that are designed to model highly-dynamic environments cannot cope with rapidly changing scenery, such as moving cloud shadows, which has different characteristics from dynamic textures. This paper presents a novel method to detect moving objects and to eliminate false alarms under moving cloud shadow regions in gray-level video sequences. The proposed method uses the relation between reflectance values of the shadowed and well-illuminated sequences of the regions in the video frame. A modified adaptive region growing approach, which extends from seed points, is designed to obtain the moving parts of the cloud shadows without presuming the geometric structure of the clouds. In order to determine the moving border of the cloud shadows, where false alarms typically occur, the cloud shadow motion should be detected. As the last stage of the proposed method, real moving objects in the scene are tried to be discriminated from false alarms by exploiting the relation of intensity ratios between the object candidate and its surroundings. The accuracy and computational efficiency of the proposed approach make it a reliable and feasible approach to be used in real-time surveillance solutions.
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Marchand, Roger, Gerald G. Mace, Thomas Ackerman, and Graeme Stephens. "Hydrometeor Detection Using Cloudsat—An Earth-Orbiting 94-GHz Cloud Radar." Journal of Atmospheric and Oceanic Technology 25, no. 4 (April 1, 2008): 519–33. http://dx.doi.org/10.1175/2007jtecha1006.1.
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Abstract In late April 2006, NASA launched Cloudsat, an earth-observing satellite that uses a near-nadir-pointing millimeter-wavelength radar to probe the vertical structure of clouds and precipitation. The first step in using Cloudsat measurements is to distinguish clouds and other hydrometeors from radar noise. In this article the operational Cloudsat hydrometeor detection algorithm is described, difficulties due to surface clutter are discussed, and several examples from the early mission are shown. A preliminary comparison of the Cloudsat hydrometeor detection algorithm with lidar-based results from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is also provided.
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Yu, W., J. Xi, Z. Wu, W. Lei, C. Zhu, and T. Tang. "A METHOD FOR EXTRACTING SUBSTATION EQUIPMENT BASED ON UAV LASER SCANNING POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 413–19. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-413-2020.
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Abstract. Smart grid construction puts higher demands on the construction of 3D models of substations. However, duo to the complex and diverse structures of substation facilities, it is still a challenge to extract the fine three-dimensional structure of the substation facilities from the massive laser point clouds. To solve this problem, this paper proposes a method for extracting substation equipment from laser scanning point clouds. Firstly, in order to improve the processing efficiency and reduce the noises, the regular voxel grid sampling method is used to down-sample the input point cloud. Furthermore, the multi-scale morphological filtering algorithm is used to segment the point cloud into ground points and non-ground points. Based on the non-ground point cloud data, the substation region is extracted using plane detection in point clouds. Then, for the filtered substation point cloud data, a three-dimensional polygon prism segmentation algorithm based on point dimension feature is proposed to extract the substation equipment. Finally, the substation LiDAR point cloud data collected by the UAV laser scanning system is used to verify the algorithm, and the qualitative and quantitative comparison analysis between the detected results and the manually extracted results are carried out. The experimental results show that the proposed method can accurately extract the substation equipment structure from the laser point cloud data. The results are consistent with the manually extracted results, which demonstrate the great potential of the proposed method in substation extraction and power system 3D modelling applications.
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Bello, Saifullahi Aminu, Shangshu Yu, Cheng Wang, Jibril Muhmmad Adam, and Jonathan Li. "Review: Deep Learning on 3D Point Clouds." Remote Sensing 12, no. 11 (May 28, 2020): 1729. http://dx.doi.org/10.3390/rs12111729.
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A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of the most significant data formats for 3D representation and are gaining increased popularity as a result of the increased availability of acquisition devices, as well as seeing increased application in areas such as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, the point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes the use of deep learning for its direct processing very challenging. This paper contains a review of the recent state-of-the-art deep learning techniques, mainly focusing on raw point cloud data. The initial work on deep learning directly with raw point cloud data did not model local regions; therefore, subsequent approaches model local regions through sampling and grouping. More recently, several approaches have been proposed that not only model the local regions but also explore the correlation between points in the local regions. From the survey, we conclude that approaches that model local regions and take into account the correlation between points in the local regions perform better. Contrary to existing reviews, this paper provides a general structure for learning with raw point clouds, and various methods were compared based on the general structure. This work also introduces the popular 3D point cloud benchmark datasets and discusses the application of deep learning in popular 3D vision tasks, including classification, segmentation, and detection.
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Liu, L., J. Li, Y. Wang, Y. Xiao, W. Zhang, and S. Zhang. "THIN CLOUD DETECTION METHOD BY LINEAR COMBINATION MODEL OF CLOUD IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1079–83. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1079-2018.
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The existing cloud detection methods in photogrammetry often extract the image features from remote sensing images directly, and then use them to classify images into cloud or other things. But when the cloud is thin and small, these methods will be inaccurate. In this paper, a linear combination model of cloud images is proposed, by using this model, the underlying surface information of remote sensing images can be removed. So the cloud detection result can become more accurate. Firstly, the automatic cloud detection program in this paper uses the linear combination model to split the cloud information and surface information in the transparent cloud images, then uses different image features to recognize the cloud parts. In consideration of the computational efficiency, AdaBoost Classifier was introduced to combine the different features to establish a cloud classifier. AdaBoost Classifier can select the most effective features from many normal features, so the calculation time is largely reduced. Finally, we selected a cloud detection method based on tree structure and a multiple feature detection method using SVM classifier to compare with the proposed method, the experimental data shows that the proposed cloud detection program in this paper has high accuracy and fast calculation speed.
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Ye, Qin, Pengcheng Shi, Kunyuan Xu, Popo Gui, and Shaoming Zhang. "A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR." Sensors 20, no. 8 (April 17, 2020): 2299. http://dx.doi.org/10.3390/s20082299.
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Reducing the cumulative error is a crucial task in simultaneous localization and mapping (SLAM). Usually, Loop Closure Detection (LCD) is exploited to accomplish this work for SLAM and robot navigation. With a fast and accurate loop detection, it can significantly improve global localization stability and reduce mapping errors. However, the LCD task based on point cloud still has some problems, such as over-reliance on high-resolution sensors, and poor detection efficiency and accuracy. Therefore, in this paper, we propose a novel and fast global LCD method using a low-cost 16 beam Lidar based on “Simplified Structure”. Firstly, we extract the “Simplified Structure” from the indoor point cloud, classify them into two levels, and manage the “Simplified Structure” hierarchically according to its structure salience. The “Simplified Structure” has simple feature geometry and can be exploited to capture the indoor stable structures. Secondly, we analyze the point cloud registration suitability with a pre-match, and present a hierarchical matching strategy with multiple geometric constraints in Euclidean Space to match two scans. Finally, we construct a multi-state loop evaluation model for a multi-level structure to determine whether the two candidate scans are a loop. In fact, our method also provides a transformation for point cloud registration with “Simplified Structure” when a loop is detected successfully. Experiments are carried out on three types of indoor environment. A 16 beam Lidar is used to collect data. The experimental results demonstrate that our method can detect global loop closures efficiently and accurately. The average global LCD precision, accuracy and negative are approximately 0.90, 0.96, and 0.97, respectively.
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Formenton, M., G. Panegrossi, D. Casella, S. Dietrich, A. Mugnai, P. Sanò, F. Di Paola, H. D. Betz, C. Price, and Y. Yair. "Using a cloud electrification model to study relationships between lightning activity and cloud microphysical structure." Natural Hazards and Earth System Sciences 13, no. 4 (April 24, 2013): 1085–104. http://dx.doi.org/10.5194/nhess-13-1085-2013.
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Abstract. In this study a one-dimensional numerical cloud electrification model, called the Explicit Microphysics Thunderstorm Model (EMTM), is used to find quantitative relationships between the simulated electrical activity and microphysical properties in convective clouds. The model, based on an explicit microphysics scheme coupled to an ice–ice noninductive electrification scheme, allows us to interpret the connection of cloud microphysical structure with charge density distribution within the cloud, and to study the full evolution of the lightning activity (intracloud and cloud-to-ground) in relation to different environmental conditions. Thus, we apply the model to a series of different case studies over continental Europe and the Mediterranean region. We first compare, for selected case studies, the simulated lightning activity with the data provided by the ground-based Lightning Detection Network (LINET) in order to verify the reliability of the model and its limitations, and to assess its ability to reproduce electrical activity consistent with the observations. Then, using all simulations, we find a correlation between some key microphysical properties and cloud electrification, and derive quantitative relationships relating simulated flash rates to minimum thresholds of graupel mass content and updrafts. Finally, we provide outlooks on the use of such relationships and comments on the future development of this study.
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Wang, Lei, Yang Chen, Luliang Tang, Rongshuang Fan, and Yunlong Yao. "Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers." Water 10, no. 11 (November 15, 2018): 1666. http://dx.doi.org/10.3390/w10111666.
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Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55–1.75 µm band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of “salt-and-pepper” in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods.
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Wu, Ke, Wenzhong Shi, and Wael Ahmed. "Structural Elements Detection and Reconstruction (SEDR): A Hybrid Approach for Modeling Complex Indoor Structures." ISPRS International Journal of Geo-Information 9, no. 12 (December 19, 2020): 760. http://dx.doi.org/10.3390/ijgi9120760.
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We present a hybrid approach for modeling complex interior structural elements from the unstructured point cloud without additional information. The proposed approach focuses on an integrated modeling strategy that can reconstruct structural elements and keep the balance of model completeness and quality. First, a data-driven approach detects the complete structure points of indoor scenarios including the curved wall structures and detailed structures. After applying the down-sampling process to point cloud dataset, ceiling and floor points are detected by RANSAC. The ceiling boundary points are selected as seed points of the growing algorithm to acquire points related to the wall segments. Detailed structures points are detected using the Grid-Slices analysis approach. Second, a model-driven refinement is conducted to the structure points that aims to decrease the impact of point cloud accuracy on the quality of the model. RANSAC algorithm is implemented to detect more accurate layout, and the hole in structure points is repaired in this refinement step. Lastly, the Screened Poisson surface reconstruction approach is conducted to generate the model based on the structure points after refinement. Our approach was validated on the backpack laser dataset, handheld laser dataset, and synthetic dataset, and experimental results demonstrate that our approach can preserve the curved wall structures and detailed structures in the model with high accuracy.
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Guo, Yanan, Xiaoqun Cao, Bainian Liu, and Mei Gao. "Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network." Symmetry 12, no. 6 (June 25, 2020): 1056. http://dx.doi.org/10.3390/sym12061056.
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Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research.
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Zeng, S., Q. Zhang, I. Jiménez-Serra, B. Tercero, X. Lu, J. Martín-Pintado, P. de Vicente, V. M. Rivilla, and S. Li. "Cloud–cloud collision as drivers of the chemical complexity in Galactic Centre molecular clouds." Monthly Notices of the Royal Astronomical Society 497, no. 4 (July 29, 2020): 4896–909. http://dx.doi.org/10.1093/mnras/staa2187.
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ABSTRACT G+0.693-0.03 is a quiescent molecular cloud located within the Sagittarius B2 (Sgr B2) star-forming complex. Recent spectral surveys have shown that it represents one of the most prolific repositories of complex organic species in the Galaxy. The origin of such chemical complexity, along with the small-scale physical structure and properties of G+0.693-0.03, remains a mystery. In this paper, we report the study of multiple molecules with interferometric observations in combination with single-dish data in G+0.693-0.03. Despite the lack of detection of continuum source, we find small-scale (0.2 pc) structures within this cloud. The analysis of the molecular emission of typical shock tracers such as SiO, HNCO, and CH3OH unveiled two molecular components, peaking at velocities of 57 and 75 km s−1. They are found to be interconnected in both space and velocity. The position–velocity diagrams show features that match with the observational signatures of a cloud–cloud collision. Additionally, we detect three series of class I methanol masers known to appear in shocked gas, supporting the cloud–cloud collision scenario. From the maser emission we provide constraints on the gas kinetic temperatures (∼30–150 K) and H2 densities (104–105 cm−2). These properties are similar to those found for the starburst galaxy NGC 253 also using class I methanol masers, suggested to be associated with a cloud–cloud collision. We conclude that shocks driven by the possible cloud–cloud collision is likely the most important mechanism responsible for the high level of chemical complexity observed in G+0.693-0.03.
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Huang, Yi, Steven T. Siems, Michael J. Manton, Luke B. Hande, and John M. Haynes. "The Structure of Low-Altitude Clouds over the Southern Ocean as Seen by CloudSat." Journal of Climate 25, no. 7 (March 28, 2012): 2535–46. http://dx.doi.org/10.1175/jcli-d-11-00131.1.
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Abstract A climatology of the structure of the low-altitude cloud field (tops below 4 km) over the Southern Ocean (40°–65°S) in the vicinity of Australia (100°–160°E) has been constructed with CloudSat products for liquid water and ice water clouds. Averaging over longitude and time, CloudSat produces a roughly uniform cloud field between heights of approximately 750 and 2250 m across the extent of the domain for both winter and summer. This cloud field makes a transition from consisting primarily of liquid water at the lower latitudes to ice water at the higher latitudes. This transition is primarily driven by the gradient in the temperature, which is commonly between 0° and −20°C, rather than by direct physical observation. The uniform lower boundary is a consequence of the CloudSat cloud detection algorithm being unable to reliably separate radar returns because of the bright surface versus returns due to clouds, in the lowest four range bins above the surface. This is potentially very problematic over the Southern Ocean where the depth of the boundary layer has been observed to be as shallow as 500 m. Cloud fields inferred from upper-air soundings at Macquarie Island (54.62°S, 158.85°E) similarly suggest that the peak frequency lies between 260 and 500 m for both summer and winter. No immediate explanation is available for the uniformity of the cloud-top boundary. This lack of a strong seasonal cycle is, perhaps, remarkable given the large seasonal cycles in both the shortwave (SW) radiative forcing experienced and the cloud condensation nuclei (CCN) concentration over the Southern Ocean.
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Chen, Qingsheng, Cien Fan, Weizheng Jin, Lian Zou, Fangyu Li, Xiaopeng Li, Hao Jiang, Minyuan Wu, and Yifeng Liu. "EPGNet: Enhanced Point Cloud Generation for 3D Object Detection." Sensors 20, no. 23 (December 4, 2020): 6927. http://dx.doi.org/10.3390/s20236927.
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Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder–decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird’s eye view) object detection compared with some previous state-of-the-art methods.
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Iqbal, Irfan A., Jon Osborn, Christine Stone, and Arko Lucieer. "A Comparison of ALS and Dense Photogrammetric Point Clouds for Individual Tree Detection in Radiata Pine Plantations." Remote Sensing 13, no. 17 (September 6, 2021): 3536. http://dx.doi.org/10.3390/rs13173536.
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Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, individual tree detection (ITD) algorithms have been developed to detect and/or delineate individual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for individual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates.
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Ishida, Haruma, Kentaro Miura, Teruaki Matsuda, Kakuji Ogawara, Azumi Goto, Kuniaki Matsuura, Yoshiko Sato, and Takashi Y. Nakajima. "Investigation of Low-Cloud Characteristics Using Mesoscale Numerical Model Data for Improvement of Fog-Detection Performance by Satellite Remote Sensing." Journal of Applied Meteorology and Climatology 53, no. 10 (October 2014): 2246–63. http://dx.doi.org/10.1175/jamc-d-13-0363.1.
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AbstractThe comprehensive relationship between meteorological conditions and whether low water cloud touches the surface, particularly at sea, is examined with the goal of improving low-cloud detection by satellite. Gridpoint-value data provided by an operational mesoscale model with integration of Multifunction Transport Satellite-2 data can provide sufficient data for statistical analyses to find general parameters that can discern whether low clouds touch the surface, compensating for uncertainty due to the scarcity of observation sites at sea and the infrequent incidence of fog. The analyses reveal that surface-touching low clouds tend to have lower cloud-top heights than those not touching the surface, although the frequency distribution of cloud-top height differs by season. The bottom of the Γ > Γm layer (where Γ and Γm are the vertical gradient and the moist-adiabatic lapse rate of the potential temperature, respectively) with surface-touching low-cloud layers tends to be very low or almost attached to the surface. In contrast, the tops of low-cloud layers not touching the surface tend to occur near the bottom of the Γ > Γm layer. Mechanisms to correlate these meteorological conditions with whether low clouds touch the surface are inferred from investigations into the vertical structure of equivalent potential temperature. These results indicate that the temperature difference between cloud-top height and the surface can be an appropriate parameter to infer whether low clouds touch the surface. It is also suggested that only a little addition of meteorological ancillary data, such as the forecast sea surface temperature, to satellite data allows successful performance of the discrimination.
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Ma, Yuchi, John Anderson, Stephen Crouch, and Jie Shan. "Moving Object Detection and Tracking with Doppler LiDAR." Remote Sensing 11, no. 10 (May 14, 2019): 1154. http://dx.doi.org/10.3390/rs11101154.
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In this paper, we present a model-free detection-based tracking approach for detecting and tracking moving objects in street scenes from point clouds obtained via a Doppler LiDAR that can not only collect spatial information (e.g., point clouds) but also Doppler images by using Doppler-shifted frequencies. Using our approach, Doppler images are used to detect moving points and determine the number of moving objects followed by complete segmentations via a region growing technique. The tracking approach is based on Multiple Hypothesis Tracking (MHT) with two extensions. One is that a point cloud descriptor, Oriented Ensemble of Shape Function (OESF), is proposed to evaluate the structure similarity when doing object-to-track association. Another is to use Doppler images to improve the estimation of dynamic state of moving objects. The quantitative evaluation of detection and tracking results on different datasets shows the advantages of Doppler LiDAR and the effectiveness of our approach.
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Lacour, Adrien, Helene Chepfer, Matthew D. Shupe, Nathaniel B. Miller, Vincent Noel, Jennifer Kay, David D. Turner, and Rodrigo Guzman. "Greenland Clouds Observed in CALIPSO-GOCCP: Comparison with Ground-Based Summit Observations." Journal of Climate 30, no. 15 (August 2017): 6065–83. http://dx.doi.org/10.1175/jcli-d-16-0552.1.
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Spaceborne lidar observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO) satellite provide the first-ever observations of cloud vertical structure and phase over the entire Greenland Ice Sheet. This study leverages CALIPSO observations over Greenland to pursue two investigations. First, the GCM-Oriented CALIPSO Cloud Product ( CALIPSO-GOCCP) observations are compared with collocated ground-based radar and lidar observations at Summit, Greenland. The liquid cloud cover agrees well between the spaceborne and ground-based observations. In contrast, ground–satellite differences reach 30% in total cloud cover and 40% in cloud fraction below 2 km above ground level, due to optically very thin ice clouds (IWC < 2.5 × 10−3 g m−3) missed by CALIPSO-GOCCP. Those results are compared with satellite cloud climatologies from the GEWEX cloud assessment. Most passive sensors detect fewer clouds than CALIPSO-GOCCP and the Summit ground observations, due to different detection methods. Second, the distribution of clouds over the Greenland is analyzed using CALIPSO-GOCCP. Central Greenland is the cloudiest area in summer, at +7% and +4% above the Greenland-wide average for total and liquid cloud cover, respectively. Southern Greenland contains free-tropospheric thin ice clouds in all seasons and liquid clouds in summer. In northern Greenland, fewer ice clouds are detected than in other areas, but the liquid cloud cover seasonal cycle in that region drives the total Greenland cloud annual variability with a maximum in summer. In 2010 and 2012, large ice-sheet melting events have a positive liquid cloud cover anomaly (from +1% to +2%). In contrast, fewer clouds (−7%) are observed during low ice-sheet melt years (e.g., 2009).
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Mohammadi, Watson, and Wood. "Deep Learning-Based Damage Detection from Aerial SfM Point Clouds." Drones 3, no. 3 (August 27, 2019): 68. http://dx.doi.org/10.3390/drones3030068.
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Aerial data collection is well known as an efficient method to study the impact following extreme events. While datasets predominately include images for post-disaster remote sensing analyses, images alone cannot provide detailed geometric information due to a lack of depth or the complexity required to extract geometric details. However, geometric and color information can easily be mined from three-dimensional (3D) point clouds. Scene classification is commonly studied within the field of machine learning, where a workflow follows a pipeline operation to compute a series of engineered features for each point and then points are classified based on these features using a learning algorithm. However, these workflows cannot be directly applied to an aerial 3D point cloud due to a large number of points, density variation, and object appearance. In this study, the point cloud datasets are transferred into a volumetric grid model to be used in the training and testing of 3D fully convolutional network models. The goal of these models is to semantically segment two areas that sustained damage after Hurricane Harvey, which occurred in 2017, into six classes, including damaged structures, undamaged structures, debris, roadways, terrain, and vehicles. These classes are selected to understand the distribution and intensity of the damage. The point clouds consist of two distinct areas assembled using aerial Structure-from-Motion from a camera mounted on an unmanned aerial system. The two datasets contain approximately 5000 and 8000 unique instances, and the developed methods are assessed quantitatively using precision, accuracy, recall, and intersection over union metrics.
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Mirzazade, Ali, Cosmin Popescu, Thomas Blanksvärd, and Björn Täljsten. "Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge." Remote Sensing 13, no. 14 (July 7, 2021): 2665. http://dx.doi.org/10.3390/rs13142665.
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For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.
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Griesche, Hannes J., Kevin Ohneiser, Patric Seifert, Martin Radenz, Ronny Engelmann, and Albert Ansmann. "Contrasting ice formation in Arctic clouds: surface-coupled vs. surface-decoupled clouds." Atmospheric Chemistry and Physics 21, no. 13 (July 9, 2021): 10357–74. http://dx.doi.org/10.5194/acp-21-10357-2021.
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Abstract. In the Arctic summer of 2017 (1 June to 16 July) measurements with the OCEANET-Atmosphere facility were performed during the Polarstern cruise PS106. OCEANET comprises amongst other instruments the multiwavelength polarization lidar PollyXT_OCEANET and for PS106 was complemented with a vertically pointed 35 GHz cloud radar. In the scope of the presented study, the influence of cloud height and surface coupling on the probability of clouds to contain and form ice is investigated. Polarimetric lidar data were used for the detection of the cloud base and the identification of the thermodynamic phase. Both radar and lidar were used to detect cloud top. Radiosonde data were used to derive the thermodynamic structure of the atmosphere and the clouds. The analyzed data set shows a significant impact of the surface-coupling state on the probability of ice formation. Surface-coupled clouds were identified by a quasi-constant potential temperature profile from the surface up to liquid layer base. Within the same minimum cloud temperature range, ice-containing clouds have been observed more frequently than surface-decoupled clouds by a factor of up to 6 (temperature intervals between −7.5 and −5 ∘C, 164 vs. 27 analyzed intervals of 30 min). The frequency of occurrence of surface-coupled ice-containing clouds was found to be 2–3 times higher (e.g., 82 % vs. 35 % between −7.5 and −5 ∘C). These findings provide evidence that above −10 ∘C heterogeneous ice formation in Arctic mixed-phase clouds occurs by a factor of 2–6 more often when the cloud layer is coupled to the surface. In turn, for minimum cloud temperatures below −15 ∘C, the frequency of ice-containing clouds for coupled and decoupled conditions approached the respective curve for the central European site of Leipzig, Germany (51∘ N, 12∘ E). This corroborates the hypothesis that the free-tropospheric ice nucleating particle (INP) reservoir over the Arctic is controlled by continental aerosol. Two sensitivity studies, also using the cloud radar for detection of ice particles and applying a modified coupling state detection, both confirmed the findings, albeit with a lower magnitude. Possible explanations for the observations are discussed by considering recent in situ measurements of INP in the Arctic, of which much higher concentrations were found in the surface-coupled atmosphere in close vicinity to the ice shore.
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Wang, X., W. Li, Y. Zhu, and B. Zhao. "Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China." Atmospheric Measurement Techniques 6, no. 3 (March 1, 2013): 549–63. http://dx.doi.org/10.5194/amt-6-549-2013.
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Abstract. The existence of various land surfaces always leads to more difficulties in cloud detection based on satellite observations, especially over bright surfaces such as snow and deserts. To improve the cloud mask result over complex terrain, an unbiased, daytime cloud detection algorithm for the Visible and InfRared Radiometer (VIRR) on board the Chinese FengYun-3A polar-orbiting meteorological satellite is applied over the northwest region of China. The algorithm refers to the concept of the clear confidence level from Moderate Resolution Imaging Spectroradiometer (MODIS) and the unbiased structure of the CLoud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). Six main channels of VIRR centered at the wavelengths of 0.455, 0.63, 0.865, 1.595, 1.36, and 10.8 μm are designed to estimate the degree of a pixel's cloud contamination judged by the clear confidence level. Based on the statistical data set during four months (January, April, July, and October) in 2010, seasonal thresholds are applied to improve the accuracy of the cloud detection results. Flags depicting snow and water are also generated by the specific threshold tests for special surfaces. As shown in image inspections, the cloud detection results over snow and deserts, adopting the proposed scheme, exhibit better correlations with true-color images than the VIRR official cloud mask results do. The performance of the proposed algorithm has been evaluated in detail for four seasons in 2011, using cloud mask products from MODIS and the ground-based observations. The evaluation is based on, overall, 47 scenes collocated with MODIS and 96 individual matchups between VIRR and the ground-based observations from two weather stations located in the research region. The quantitative validations suggest that the estimations of clear-sky regions have been greatly improved by the proposed algorithm, while a poor identification of the cirrus clouds occurs over deserts.
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Maragoudaki, F., M. Kontizas, A. Dapergolas, D. H. Morgan, and E. Kontizas. "Detection and structure of stellar complexes in the large magellanic cloud." Astronomical & Astrophysical Transactions 18, no. 3 (December 1999): 487–92. http://dx.doi.org/10.1080/10556799908203005.
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Yu, Bencheng, Peisen Song, and Xiaoyuan Xu. "An android malware static detection scheme based on cloud security structure." International Journal of Security and Networks 13, no. 1 (2018): 51. http://dx.doi.org/10.1504/ijsn.2018.090643.
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Yu, Bencheng, Peisen Song, and Xiaoyuan Xu. "An android malware static detection scheme based on cloud security structure." International Journal of Security and Networks 13, no. 1 (2018): 51. http://dx.doi.org/10.1504/ijsn.2018.10011753.
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Sharma, Puneet, Peter Dalin, and Ingrid Mann. "Towards a Framework for Noctilucent Cloud Analysis." Remote Sensing 11, no. 23 (November 22, 2019): 2743. http://dx.doi.org/10.3390/rs11232743.
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In this paper, we present a framework to study the spatial structure of noctilucent clouds formed by ice particles in the upper atmosphere at mid and high latitudes during summer. We studied noctilucent cloud activity in optical images taken from three different locations and under different atmospheric conditions. In order to identify and distinguish noctilucent cloud activity from other objects in the scene, we employed linear discriminant analysis (LDA) with feature vectors ranging from simple metrics to higher-order local autocorrelation (HLAC), and histogram of oriented gradients (HOG). Finally, we propose a convolutional neural networks (CNN)-based method for the detection of noctilucent clouds. The results clearly indicate that the CNN-based approach outperforms the LDA-based methods used in this article. Furthermore, we outline suggestions for future research directions to establish a framework that can be used for synchronizing the optical observations from ground-based camera systems with echoes measured with radar systems like EISCAT in order to obtain independent additional information on the ice clouds.
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Liu, Weiping, Jia Sun, Wanyi Li, Ting Hu, and Peng Wang. "Deep Learning on Point Clouds and Its Application: A Survey." Sensors 19, no. 19 (September 26, 2019): 4188. http://dx.doi.org/10.3390/s19194188.
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Point cloud is a widely used 3D data form, which can be produced by depth sensors, such as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and irregular, many researchers focused on the feature engineering of the point cloud. Being able to learn complex hierarchical structures, deep learning has achieved great success with images from cameras. Recently, many researchers have adapted it into the applications of the point cloud. In this paper, the recent existing point cloud feature learning methods are classified as point-based and tree-based. The former directly takes the raw point cloud as the input for deep learning. The latter first employs a k-dimensional tree (Kd-tree) structure to represent the point cloud with a regular representation and then feeds these representations into deep learning models. Their advantages and disadvantages are analyzed. The applications related to point cloud feature learning, including 3D object classification, semantic segmentation, and 3D object detection, are introduced, and the datasets and evaluation metrics are also collected. Finally, the future research trend is predicted.
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Cheng, Hsu-Yung, and Chih-Lung Lin. "Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques." Atmospheric Measurement Techniques 10, no. 1 (January 17, 2017): 199–208. http://dx.doi.org/10.5194/amt-10-199-2017.
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Abstract. Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.
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Jurado, Juan M., Luís Pádua, Francisco R. Feito, and Joaquim J. Sousa. "Automatic Grapevine Trunk Detection on UAV-Based Point Cloud." Remote Sensing 12, no. 18 (September 17, 2020): 3043. http://dx.doi.org/10.3390/rs12183043.
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The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant’s structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.
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Fouladinejad, F., A. Matkan, M. Hajeb, and F. Brakhasi. "HISTORY AND APPLICATIONS OF SPACE-BORNE LIDARS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 407–14. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-407-2019.
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Abstract. LIDAR (Light Detection and Ranging) is a laser altimeter system that determines the distance by measuring pulse travel time. The data from the LIDAR systems provide unique information on the vertical structure of land covers. Compared to ground-based and airborne LIDARs providing a high-resolution digital surface model, space-borne LIDARs can provide important information about the vertical profile of the atmosphere in a global scale. The overall objective of these satellites is to study the elevation changes and the vertical distribution of clouds and aerosols. In this paper an overview on the space-borne laser scanner satellites are accomplished and their applications are introduced. The first space-borne LIDAR is the ICESat (Ice, Cloud and land Elevation Satellite) satellite carrying the GLAS instrument which was launched in January 2003. The CALIPSO (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, 2006), CATS-ISS (the Cloud-Aerosol Transport System, 2015), ADM-Aeolus (Atmospheric Dynamics Mission, 2018), and ICESat-2 (Ice, Cloud and land Elevation Satellite-2, 2018) satellites were respectively lunched and began to receive information about the vertical structure of the atmosphere and land cover. In addition, two ACE (The Aerosol-Cloud-Ecosystems, 2022) and EarthCARE (Earth Clouds, Aerosols and Radiation Explorer, 2021) space-borne satellites were planned for future. The data of the satellites are increasingly utilized to improve the numerical weather predictions (NWP) and climate modeling.
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Krawczyk, Karolina, and Janusz Jasiński. "Multispectral MODIS data for visual interpretation of fog and low layer clouds." Geodesy and Cartography 64, no. 1 (June 1, 2015): 15–27. http://dx.doi.org/10.1515/geocart-2015-0001.
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Abstract The paper presents the capability of applying selected modern remote sensing methods based on commonly available high spatial resolution MODIS images to fog and low layer clouds detection. Single spectral channel images, differential images and selected color compositions are analyzed for distinguishing the areas of the phenomena occurrence. Their internal structure and fog/cloud particles properties are assessed using brightness temperature and reflectance diagrams.
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Collischon, Caroline, Manami Sasaki, Klaus Mecke, Sean D. Points, and Michael A. Klatt. "Tracking down the origin of superbubbles and supergiant shells in the Magellanic Clouds with Minkowski tensor analysis." Astronomy & Astrophysics 653 (August 31, 2021): A16. http://dx.doi.org/10.1051/0004-6361/202040153.
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Aims. We develop an automatic bubble-recognition routine based on Minkowski functionals (MF) and tensors (MT) to detect bubble-like interstellar structures in optical emission line images. Methods. Minkowski functionals and MT are powerful mathematical tools for parameterizing the shapes of bodies. Using the papaya2-library, we created maps of the desired MF or MT of structures at a given window size. We used maps of the irreducible MT ψ2, which is sensitive to elongation, to find filamentary regions in Hα, [S II], and [O III] images of the Magellanic Cloud Emission Line Survey. Using the phase of ψ2, we were able to draw lines perpendicular to each filament and thus obtain line-density maps. This allowed us to find the center of a bubble-like structure and to detect structures at different window sizes. Results. The detected bubbles in all bands are spatially correlated to the distribution of massive stars, showing that we indeed detect interstellar bubbles without large spatial bias. Eighteen out of 59 supernova remnants in the Large Magellanic Cloud (LMC) and 13 out of 20 superbubbles are detected in at least one wavelength. The lack of detection is mostly due to surrounding emission that disturbs the detection, a too small size, or the lack of a (circular) counterpart in our emission line images. In line-density maps at larger scales, maxima can be found in regions with high star formation in the past, often inside supergiant shells (SGS). In SGS LMC 2, there is a maximum west of the shell where a collision of large gas clouds is thought to have occurred. In the Small Magellanic Cloud (SMC), bubble detection is impaired by the more complex projected structure of the galaxy. Line maps at large scales show large filaments in the SMC in a north-south direction, especially in the [S II] image. The origin of these filaments is unknown.
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Rosenfeld, D., and Y. Levi. "Using satellite data multispectral analysis for detection of cloud top microphysical structure." Journal of Aerosol Science 28, no. 7 (October 1997): 1355. http://dx.doi.org/10.1016/s0021-8502(97)90113-0.
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Yuan, Feng, Yee Hui Lee, Yu Song Meng, and Jin Teong Ong. "Water Vapor Pressure Model for Cloud Vertical Structure Detection in Tropical Region." IEEE Transactions on Geoscience and Remote Sensing 54, no. 10 (October 2016): 5875–83. http://dx.doi.org/10.1109/tgrs.2016.2574744.
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Vernier, J. P., T. D. Fairlie, J. J. Murray, A. Tupper, C. Trepte, D. Winker, J. Pelon, et al. "An Advanced System to Monitor the 3D Structure of Diffuse Volcanic Ash Clouds." Journal of Applied Meteorology and Climatology 52, no. 9 (September 2013): 2125–38. http://dx.doi.org/10.1175/jamc-d-12-0279.1.
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AbstractMajor disruptions of the aviation system from recent volcanic eruptions have intensified discussions about and increased the international consensus toward improving volcanic ash warnings. Central to making progress is to better discern low volcanic ash loadings and to describe the ash cloud structure more accurately in three-dimensional space and time. Here, dispersed volcanic ash observed by the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) space-based lidar near 20 000–40 000 ft [~(6–13) km] over Australia and New Zealand during June 2011 is studied. This ash event took place 3 weeks after the Puyehue-Cordon Caulle eruption, which disrupted air traffic in much of the Southern Hemisphere. The volcanic ash layers are shown to exhibit color ratios (1064/532 nm) near 0.5, significantly lower than unity, as is observed with ice. Those optical properties are used to develop an ash detection algorithm. A “trajectory mapping” technique is then demonstrated wherein ash cloud observations are ingested into a Lagrangian model and used to construct ash dispersion maps and cross sections. Comparisons of the model results with independent observations suggest that the model successfully reproduces the 3D structure of volcanic ash clouds. This technique has a potential operational application in providing important additional information to worldwide volcanic ash advisory centers.
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LaRoche, Kendell T., and Timothy J. Lang. "Observations of Ash, Ice, and Lightning within Pyrocumulus Clouds Using Polarimetric NEXRAD Radars and the National Lightning Detection Network." Monthly Weather Review 145, no. 12 (December 2017): 4899–910. http://dx.doi.org/10.1175/mwr-d-17-0253.1.
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A pyrocumulus is a convective cloud that can develop over a wildfire. Under certain conditions, pyrocumulus clouds become vertically developed enough to produce lightning. NEXRAD dual-polarization weather radar and upgraded National Lightning Detection Network (NLDN) data were used to analyze 10 case studies of ash plumes and pyrocumulus clouds from 2013 that either did or did not produce detected lightning. Past research has shown that pyrocumulus cases are most likely to produce lightning when there is a decrease in differential reflectivity (toward 0 dB) and an increase in the correlation coefficient (to >0.8), as measured by polarimetric radar, due to the transition from pure smoke/ash to frozen hydrometeors. All pyrocumulus cases that produced lightning in this study displayed the polarimetric characteristics of rimed ice within their respective clouds. Time series analysis of radar-inferred ash and rimed ice volumes within ash plumes and pyrocumulus clouds showed that NLDN-detected lightning occurred only after the cloud contained significant amounts of precipitation-sized rimed ice. The results suggest that the recently dual-pol-enabled NEXRADs and the more sensitive NLDN network can be used to explore ash plume and pyrocumulus microphysical structure and lightning production.
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Xie, Wanyi, Dong Liu, Ming Yang, Shaoqing Chen, Benge Wang, Zhenzhu Wang, Yingwei Xia, Yong Liu, Yiren Wang, and Chaofang Zhang. "SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation." Atmospheric Measurement Techniques 13, no. 4 (April 17, 2020): 1953–61. http://dx.doi.org/10.5194/amt-13-1953-2020.
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Abstract. Cloud detection and cloud properties have substantial applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step in deriving cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds and often fail to achieve satisfactory performance. Deep convolutional neural networks (CNNs) can extract high-level feature information of objects and have achieved remarkable success in many image segmentation fields. On this basis, a novel deep CNN model named SegCloud is proposed and applied for accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses a symmetric encoder–decoder structure. The encoder network combines low-level cloud features to form high-level, low-resolution cloud feature maps, whereas the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The Softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination capability and can automatically segment whole-sky images obtained by a ground-based all-sky-view camera. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods do. The accuracy and practicability of SegCloud are further proven by applying it to cloud cover estimation.
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Blanchard, Yann, Jacques Pelon, Edwin W. Eloranta, Kenneth P. Moran, Julien Delanoë, and Geneviève Sèze. "A Synergistic Analysis of Cloud Cover and Vertical Distribution from A-Train and Ground-Based Sensors over the High Arctic Station Eureka from 2006 to 2010." Journal of Applied Meteorology and Climatology 53, no. 11 (November 2014): 2553–70. http://dx.doi.org/10.1175/jamc-d-14-0021.1.
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AbstractActive remote sensing instruments such as lidar and radar allow one to accurately detect the presence of clouds and give information on their vertical structure and phase. To better address cloud radiative impact over the Arctic area, a combined analysis based on lidar and radar ground-based and A-Train satellite measurements was carried out to evaluate the efficiency of cloud detection, as well as cloud type and vertical distribution, over the Eureka station (80°N, 86°W) between June 2006 and May 2010. Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat data were first compared with independent ground-based cloud measurements. Seasonal and monthly trends from independent observations were found to be similar among all datasets except when compared with the weather station observations because of the large reported fraction of ice crystals suspended in the lower troposphere in winter. Further investigations focused on satellite observations that are collocated in space and time with ground-based data. Cloud fraction occurrences from ground-based instruments correlated well with both CALIPSO operational products and combined CALIPSO–CloudSat retrievals, with a hit rate of 85%. The hit rate was only 77% for CloudSat products. The misdetections were mainly attributed to 1) undetected low-level clouds as a result of sensitivity loss and 2) missed clouds because of the distance between the satellite track and the station. The spaceborne lidar–radar synergy was found to be essential to have a complete picture of the cloud vertical profile down to 2 km. Errors are quantified and discussed.
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Boerner, R., L. Hoegner, and U. Stilla. "VOXEL BASED SEGMENTATION OF LARGE AIRBORNE TOPOBATHYMETRIC LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 107–14. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-107-2017.
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Point cloud segmentation and classification is currently a research highlight. Methods in this field create labelled data, where each point has additional class information. Current approaches are to generate a graph on the basis of all points in the point cloud, calculate or learn descriptors and train a matcher for the descriptor to the corresponding classes. Since these approaches need to look on each point in the point cloud iteratively, they result in long calculation times for large point clouds. Therefore, large point clouds need a generalization, to save computation time. One kind of generalization is to cluster the raw points into a 3D grid structure, which is represented by small volume units ( i.e. voxels) used for further processing. This paper introduces a method to use such a voxel structure to cluster a large point cloud into ground and non-ground points. The proposed method for ground detection first marks ground voxels with a region growing approach. In a second step non ground voxels are searched and filtered in the ground segment to reduce effects of over-segmentations. This filter uses the probability that a voxel mostly consist of last pulses and a discrete gradient in a local neighbourhood . The result is the ground label as a first classification result and connected segments of non-ground points. The test area of the river Mangfall in Bavaria, Germany, is used for the first processing.
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Lamer, K., A. Tatarevic, I. Jo, and P. Kollias. "Evaluation of gridded Scanning ARM Cloud Radar reflectivity observations and vertical Doppler velocity retrievals." Atmospheric Measurement Techniques Discussions 6, no. 6 (November 8, 2013): 9579–621. http://dx.doi.org/10.5194/amtd-6-9579-2013.
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Abstract. The Scanning ARM Cloud Radars (SACR's) provide continuous atmospheric observations aspiring to capture the 3-D cloud-scale structure. Sampling clouds in 3-D is challenging due to their temporal-spatial scales, the need to sample the sky at high elevations and cloud radar limitations. Thus, a common scan strategy is to repetitively slice the atmosphere from horizon to horizon as clouds advect over the radar (Cross-Wind Range Height Indicator – CWRHI). Here, the processing and gridding of the SACR CW-RHI scans are presented. First, the SACR sample observations from the ARM Oklahoma (SGP) and Cape-Cod (PVC) sites are post-processed (detection mask, velocity de-aliasing and gaseous attenuation correction). The resulting radial Doppler moment fields are then mapped to Cartesian coordinates with time as one of the dimension. The Cartesian-gridded Doppler velocity fields are next decomposed into the horizontal wind velocity contribution and the vertical Doppler velocity component. For validation purposes, all gridded and retrieved fields are compared to collocated zenith pointing ARM cloud radar measurements. We consider that the SACR sensitivity loss with range, the cloud type observed and the research purpose should be considered in determining the gridded domain size. Our results also demonstrate that the gridded SACR observations resolve the main features of low and high stratiform clouds. It is established that the CW-RHI observations complemented with processing techniques could lead to robust 3-D clouds dynamical representations up to 25–30° off zenith. The proposed gridded products are expected to advance our understanding of 3-D cloud morphology, dynamics, anisotropy and lead to more realistic 3-D radiative transfer calculations.
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Mohammadi, Mohammad Ebrahim, Richard L. Wood, and Christine E. Wittich. "Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures." ISPRS International Journal of Geo-Information 8, no. 12 (November 26, 2019): 527. http://dx.doi.org/10.3390/ijgi8120527.
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Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged areas. Two synthetic datasets with planar and cylindrical geometries with known ground truth damage were created and used to test the developed workflow. In addition, the developed method was further validated on three real-world point cloud datasets using lidar and structure-from-motion techniques, which represented different underlying geometries and exhibited varying severity and mechanisms of damage. The analysis of the synthetic datasets demonstrated the robustness of the proposed damage detection method to classify vertices as surface damage with high recall and precision rates and a low false-positive rate. The real-world datasets illustrated the scalability of the damage detection method and its ability to classify areas as damaged and undamaged at the centimeter level. Moreover, the output classification of the damage detection method automatically bins the damaged vertices into different confidence intervals for further classification of detected likely damaged areas. Moving forward, the presented workflow can be used to bolster structural inspections by reducing subjectivity, enhancing reliability, and improving quantification in surface-evident damage.
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Azzopardi, M., and E. Rebeirot. "Large-scale structure and kinematics of the Magellanic Clouds from carbon star studies." Symposium - International Astronomical Union 148 (1991): 71–76. http://dx.doi.org/10.1017/s0074180900200041.
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Initially, carbon stars have been searched for through their CN bands in the near infrared. However, C stars can also be identified efficiently by the C2 Swan bands in the blue-green spectral region. Schmidt telescopes with objective-prisms have been used intensively for the detection of C stars. More recently, transmission gratings at the prime foci of large telescopes have made possible deeper surveys at limiting magnitudes that insure carbon star detection in the Magellanic Clouds (MCs) with a reasonable degree of completeness; completeness is even better in the regions where both observational techniques have been used. Statistical studies are now possible. The C star surface distribution and global kinematics derived from radial velocity measurements of selected C stars, when compared to those of other objects, show that the field Small Magellanic Cloud (SMC) C stars are not a young population, in agreement with studies of C stars in SMC clusters.