Convective Cloud Detection and Tracking from Series of Infrared Images
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RESEARCH ARTICLE
Convective Cloud Detection and Tracking from Series of Infrared Images Barnali Goswami & Gupinath Bhandari
Received: 27 February 2012 / Accepted: 15 August 2012 / Published online: 17 October 2012 # Indian Society of Remote Sensing 2012
Abstract The most significant part of prediction of precipitation is the detection and identification of convective (cumulonimbus) clouds, also the tracking of cloud movement is important for identification of location of precipitation. A very simple methodology for detecting convective clouds and then tracking its movement from a series of infrared (IR) images is proposed in this paper. IR image is segmented using k-means clustering algorithm, which has been implemented using Euclidean, Manhattan and Mahalanobis distances and the results have been compared. Cloud clusters have been identified from segmented image and subsequently the large clusters were extracted. Center of Mass (CoM) was calculated for each selected cloud cluster and its position after every 30 min was predicted and compared with the actual values. If the predicted position deviates, the proposed models automatically adjusts itself, and the next prediction becomes closer to original values of position.
B. Goswami (*) Department of Computer Applications, Narula Institute of Technology, Kolkata, India e-mail: [email protected] G. Bhandari Department of Civil Engineering, Jadavpur University, Kolkata, India e-mail: [email protected]
Keywords Convective clouds . Infrared . Clustering . Tracking
Introduction Detection and identification of convective clouds play a key role in predicting precipitation that can eventually lead to flood in a particular area. Also tracking of convective clouds is a very crucial step for nowcasting precipitation (Das et al. 2009). The convective clouds can be identified from thermal infrared (TIR) images (10.5–12.5 μm) because clouds are associated with extremely low temperature (Mandal et al. 2005; Turiel et al. 2005). It can be tracked from a given sequence of satellite images and then can be used for weather now-casting (Vila et al. 2008). Ample numbers of literature are available in the area of cloud detection from satellite images. Most of the existing methods used multi-spectral band images (like visible, middle infrared, thermal infrared) to extract cloud microphysical parameters like cloud optical thickness, effective particle radius, cloud top temperature and liquid water path (King et al. 2004; Chen et al. 2008; Zinner et al. 2008; Kiihnlein et al. 2010). These methods are very effective in detecting convective clouds. But the major problem is that the detection cannot be done in real time or near real time as they require processing of huge data set. Another existing method to identify the convective clouds is based on the temperature difference between the TIR and water vapour
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channel images (Bessho et al. 2001; Mosher 2002, 2003; Yaping et al. 2008). The main disadvantage of this approach is that it may wrongly identify cirrus as cumulon
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