Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Yet, because satellite image data are in increasingly higher resolution, both spatially and temporally, meteorologists cannot fully leverage the data in their forecasts. Automatic satellite image analysis methods that can find storm-related cloud patterns are thus in demand. We propose a machine learning and pattern recognition-based approach to detect “comma-shaped” clouds in satellite images, which are specific cloud distribution patterns strongly associated with cyclone formulation. In order to detect regions with the targeted movement patterns, we use manually annotated cloud examples represented by both shape and motion-sensitive features to train the computer to analyze satellite images. Sliding windows in different scales ensure the capture of dense clouds, and we implement effective selection rules to shrink the region of interest among these sliding windows. Finally, we evaluate the method on a hold-out annotated comma-shaped cloud data set and cross match the results with recorded storm events in the severe weather database. The validated utility and accuracy of our method suggest a high potential for assisting meteorologists in weather forecasting.
Cloud motion estimation is also an important method for storm detection, and a common approach estimates cloud movements through cross-correlation over adjacent images. Some earlier work @cite_15 and @cite_17 applied the cross-correlation method to derive the motion vectors from cloud textures, which was later extended to multi-channel satellite images in @cite_19 . The cross-correlation method could partly characterize the airflow dynamics of the atmosphere and provide meaningful speed and direction information on large areas @cite_38 . After being introduced in the radar reflectivity images, the method was applied in the automatic cloud-tracking systems using satellite images. A later work @cite_40 implemented the cross-correlation in predicting and tracking the Mesoscale Convective Systems (MCS, a type of storms). Their motion vectors were computed by aggregating nearby pixels at two consecutive frames; thus, they are subject to spatially smoothed effects and miss fine-grained details. Inspired by the ideas of motion interpretation, we define a novel correlation aiming to recognize cloud motion patterns in a longer period. The combination of motion and shape features demonstrates high classification accuracy on our manually labeled dataset.
@cite_38 @cite_19 @cite_40 @cite_15 @cite_17
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Abstract Cloud motion can be determined from sequential pictures obtained from the geosynchronous Applications Technology Satellites, ATS-I and ATS-III. Experiments have been conducted using two automated techniques for computing the cloud motions over the time interval between two digitized pictures. One technique is a direct application of cross-correlation using the fast Fourier Transform (fFT). In the other, a relatively simple matching technique is used in the computer to recognize binary images of cloud fields on each member of a picture pair. Results obtained from the two techniques are similar. The computed directions are in good agreement with those determined by manual methods with indications that computed speeds are more accurately resolved than those determined manually. This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames share some similarities, motion estimation is key to effective compression of these sequences. It, however, remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the point clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature-matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring a significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way. Tracking both structure and motion of nonrigid objects from monocular images is an important problem in vision. In this paper, a hierarchical method which integrates local analysis (that recovers small details) and global analysis (that appropriately limits possible nonrigid behaviors) is developed to recover dense depth values and nonrigid motion from a sequence of 2D satellite cloud images without any prior knowledge of point correspondences. This problem is challenging not only due to the absence of correspondence information but also due to the lack of depth cues in the 2D cloud images (scaled orthographic projection). In our method, the cloud images are segmented into several small regions and local analysis is performed for each region. A recursive algorithm is proposed to integrate local analysis with appropriate global fluid model constraints, based on which a structure and motion analysis system, SMAS, is developed. We believe that this is the first reported system in estimating dense structure and nonrigid motion under scaled orthographic views using fluid model constraints. Experiments on cloud image sequences captured by meteorological satellites (GOES-8 and GOES-9) have been performed using our system, along with their validation and analyses. Both structure and 3D motion correspondences are estimated to subpixel accuracy. Our results are very encouraging and have many potential applications in earth and space sciences, especially in cloud models for weather prediction. Cloud motion vectors derived from sequences of remotely sensed data are widely used by numerical weather prediction models and other meteorological and climatic applications. One approach to computing cloud motion vectors is the correlation-relaxation labeling technique, in which a set of candidate vectors for each template is refined using relaxation labeling to provide a local smoothness constraint. In this letter, an extension of the correlation-relaxation labeling framework to tracking clouds in multichannel imagery is presented. As this multichannel approach takes advantage of the diversity between channels, it has the potential for producing motion vectors with a superior quality and coverage than can be achieved by any individual channel. Results for visible and infrared images from Meteostat Second Generation confirm the benefits of the multichannel approach