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 segmentation is an important method for detecting storm cells. Lakshmanan @cite_42 proposed a hierarchical cloud-texture segmentation method for satellite image. Later, they improved the method by applying watershed transform to the segmentation and using pixel intensity thresholding to identify storms @cite_31 . However, brightness temperature in a single satellite image is easily affected by lighting conditions, geographical location, and satellite imager quality, which is not fully considered in the thresholding-based methods. Therefore, we consider these spatial and temporal factors and segment the high cloud part based on the Gaussian Mixture Model (GMM).
@cite_31 @cite_42
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Automatic cloud detection from satellite imagery is a necessary preprocessing step in remote sensing. Given that humans can easily “see” clouds in an image because of salient region features, we adopt a visual attention technique in computer vision to automatically identify images with a significant cloud cover. The proposed method generates a rough cloud mask by using a top-down visual saliency model to qualitatively distinguish cloud images from noncloud images. First, an image is downsized for rapid processing. Some basic saliency maps of clouds are then generated by multilevel segmentation, the computation of cloud visual saliency features, and feature classification. Thereafter, we fuse the basic saliency maps by using a most-votes-win strategy to generate the cloud mask. With the cloud mask, a threshold is used to classify the images as cloud or noncloud images. A total of 200 RapidEye images are tested by using the algorithm. Of the cloud images, 92 are correctly identified. The average processing time is 1.8 s per image. Automatic cloud extraction from satellite imagery is an important task for many applications in remote sensing. Humans can easily identify various clouds from satellite images based on the visual features of cloud. In this study, a method of automatic cloud detection is proposed based on object classification of image features. An image is first segmented into superpixels so that the descriptor of each superpixel can be computed to form a feature vector for classification. The support vector machine algorithm is then applied to discriminate cloud and noncloud regions. Thereafter, the GrabCut algorithm is used to extract more accurate cloud regions. The key of the method is to deal with the highly varying patterns of clouds. The bag-of-words (BOW) model is used to construct the compact feature vectors from densely extracted local features, such as dense scale-invariant feature transform (SIFT). The algorithm is tested using 101 RapidEye and 86 Landsat images with many cloud patterns. These images achieve 89.2 of precision, 87.8 of recall for RapidEye, 85.8 of precision, and 83.9 of recall for Landsat. The experiments show that the method is insensitive to the number of codewords in the codebook construction of the BOW. 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 detection plays an essential role in meteorological research and has received considerable attention in recent years. However, this issue is particularly challenging due to the diverse characteristics of clouds. In this letter, a novel algorithm based on superpixel segmentation (SPS) is proposed for cloud detection. In our proposed strategy, a series of superpixels could be obtained adaptively by SPS algorithm according to the characteristics of clouds. We first calculate a local threshold for each superpixel and then determine a threshold matrix for the whole image. Finally, cloud can be detected by comparing with the obtained threshold matrix. Experimental results show that our proposed algorithm achieves better performance than the current cloud detection algorithms.