This document discusses a system for automatically classifying satellite images to monitor weather patterns and provide early warnings of adverse weather. It describes segmenting images, calculating centroids and distances between regions to identify cloud movement, and setting an alarm if clouds move in a way that indicates potential adverse weather. Algorithms are provided for displaying images, segmenting regions of interest, computing centroids and distances, matching images to estimate cloud models, determining cloud proximity above earth regions, and setting an alarm based on cloud movement patterns. The goal is to develop an early warning system by analyzing successive satellite images at hourly intervals and predicting future cloud patterns and weather.
3. Abstract In this paper we discuss on a system where in automatically the images collected from the satellite are classified into either normal weather patterns or adverse weather patterns developed. An alarm used to rise as early warning if a tendency of adverse weather system is about to be formed. This requires the domain knowledge about the cloud formation, movement of clouds, and image processing techniques. We discuss here regarding the displaying and analyzing the satellite image data, image segmentation which subdivides an image into its constituent regions/objects, and attempting to achieve the goals of early warning system.
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5. Diagram of Object Recognition Relations between PR and IP Diagram of Object Recognition Image Processing Images Data Analysis Pattern Recognition Pattern Recognition Object Classes Image Processing (IP) Pattern Recognition (PR) Object Classes Images
10. Processing the whole image is computationally expensive. So for our further processing we considered only 100X100 pixel. We give a search area by giving X and Y coordinates and increases the X and Y coordinate vale respectively. The region of interest is on the specific area so our interest will be more on that area so we segment that regions for our further process.
11. New image is pointed on the old image to enable the user to instantaneously compare the subsequent images. Algorithm: Segmentation Step 1 : Using image display algorithm displays the image. Step 2 : Initialize two seeding point. Step 3 : Select first seeding point and grow the region, similarly do same for second seeding point . Step 4 : Display those segment using Display Algorithm.
16. Message & Value of pixels Calculations Centroid Distance Alarm Image Segment Proximity Image matching FLOW OF PROCESS FOR IDENTIFICATION OF MOVEMENTS OF CLOUDS MODEL
19. Centroid C=(51.92,58.8) Centroid of region A No of rows=100, No of columns=200 No of pixels= 52 Centroid C=(Xstartpoint+X, Ystartpoint+Y) (50+1.92,55+3.8) Showing the centroid of the region A
20. Centroid of region B Centroid = (55.75,80) Similarly we calculate Centroid for Region B Showing the centroid of the region B
23. Centroid of region B1 Centroid = (70.77,80) Showing the centroid of the region B1
24. Distance between reference image and the next successive region B1 Region B1 Region A Distan ce D1 Showing the distance between reference image and the next cloud region
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26. ALGORITHM : Image matching Input : File B- reference image of cloud region. File B1- cloud region of new image. Or Two 2D arrays to retrieve values of files, (X2,Y2)- Centroid of reference Cloud region. (X1,Y1)- Centroid of reference Earth region Output : Output- 2D array to store the difference of two images. Method : Subtract the two images and store the resultant in Output. Find the centroid of resultant image. Find the shift(X2out=X2+X211,Y2out=Y2+Y211). Find the distance between the new Cloud region and reference Earth region using distance algorithm.
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31. The Ultimate goal of the project is to give warning message about the status of clouds. The Algorithm for Alarm follows MESSAGE
37. We took the following Spacecraft images for the project and the output we have seen in previous slides. The last two images show that we have taken images every 1-hour of time.
