2,545 research outputs found
Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS
Being able to effectively identify clouds and monitor their evolution is one
important step toward more accurate quantitative precipitation estimation and
forecast. In this study, a new gradient-based cloud-image segmentation
technique is developed using tools from image processing techniques. This
method integrates morphological image gradient magnitudes to separable cloud
systems and patches boundaries. A varying scale-kernel is implemented to reduce
the sensitivity of image segmentation to noise and capture objects with various
finenesses of the edges in remote-sensing images. The proposed method is
flexible and extendable from single- to multi-spectral imagery. Case studies
were carried out to validate the algorithm by applying the proposed
segmentation algorithm to synthetic radiances for channels of the Geostationary
Operational Environmental Satellites (GOES-R) simulated by a high-resolution
weather prediction model. The proposed method compares favorably with the
existing cloud-patch-based segmentation technique implemented in the
PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using
Artificial Neural Network - Cloud Classification System) rainfall retrieval
algorithm. Evaluation of event-based images indicates that the proposed
algorithm has potential to improve rain detection and estimation skills with an
average of more than 45% gain comparing to the segmentation technique used in
PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to
98%
Automated Cloud Patch Segmentation of FY-2C Image Using Artificial Neural Network and Seeded Region Growing Method (ANN-SRG)
This paper presents a new algorithm Artificial Neural Network and Seeded Region Growing (ANN-SRG) to segment cloud patches of different types. This method used Seeded Region Growing (SRG) as segmentation algorithm, and Artificial Neural Network (ANN) Cloud classification as preprocessing algorithm. It can be trained to respond favorably to cloud types of interest, and SRG method is no longer sensitive to the seeds selection and growing rule. To illustrate the performance of this technique, this paper applied it on Chinese first operational geostationary meteorological satellite FengYun-2C (FY-2C) in three infrared channels (IR1, 10.3- 11.33BC;m; IR2, 11.5-12.53BC;m and WV 6.3-7.63BC;m) with 2864 samples collected by meteorologists in June, July, and August in 2007. The result shows that this method can distinguish and segment cloud patches of different types, and improves the traditional SRG algorithm by reducing the uncertainty of seeds extraction and regional growth
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Self-organizing nonliner output map (SONO): An artificial neural network suitable for cloud-patch based rainfall estimation
The state-of-the-art progress in cloud detection, identification, and tracking approaches: a systematic review
A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too
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