Automatic Classification Satellite images for weather Monitoring

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"Automatic classification of Satellite Images for Weather Monitoring"

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.

INTRODUCTION The Spacecraft orbits experiences disturbances due to radiation of the sun, moon or even because of the solar radiation pressure. These deviations are determined using ranging and tracking data, and are corrected periodically once in 80 days. The orbit deviations are restricted to less than +/-0.1 deg. Weather is the state of atmosphere at a particular time and place. Climate is what we expect and Weather is what we get. If Weather is the watch then climate is the calendar. SATELLITE IMAGES: INSAT-2E VHRR CCD INSATS provide continuous monitoring of weather pattern through a series of satellites located over Indian Ocean region. Relevance of PR & IP: Spatial Technique: Problem Statement:

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

HOW TO DISPLAY SATELLITE IMAGE AND WHY? We are interested in displaying and analyzing satellite image data actively participating in scientific visualization in such a way as to gain, understanding and insight into the data. In visualization we are seeking to understand the data. To capitalize on this talent by providing satellite image data i a format that can be interpreted to gain new insight about Earth. The first problem is that the satellite data collected by government agencies are in digital format. How to convert the brightness values is the creation of brightness map also commonly refers to as gray scale image. Satellite images are compact and very sensitive to distortion.

Algorithm: Display Step 1 : Scan the image and store the gray values in the file. Step 2 : Retrieve the file and using Graphic User Interface display the corresponding pixel of the gray values. The image is displayed on the screen line by line and the whole image is printed on the screen. If there is any loss in the image data, that will be left blank without disturbing the geometry of the image. The values obtained are stored in files and retrieved in 2 dimensional arrays to manipulate.

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.

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.

Image considered for processing of size 255 X 255 pixels

Region B (100X100) Region A (100X100) Segmented Image After Segmentation twice for the region of interest (Region A and region B)

Identification of Movement of Clouds Dealing with indefinite shapes

NEED: MODEL: In this system we will first find the centroid for the region we segment. Using these two centroids of the regions we will found distance between the regions. These centroid and distance are considered as reference centroid and distance of the reference image. Here region A is assumed as Earth region and region B is assumed as Cloud region.

Message & Value of pixels Calculations Centroid Distance Alarm Image Segment Proximity Image matching FLOW OF PROCESS FOR IDENTIFICATION OF MOVEMENTS OF CLOUDS MODEL

Method for computing the Centroid : ALGORITHM : Centroid Input : Segmented region(image), means two dimensional array which contains gray values of the image. Output : C, Centroid which is numeric. C=(X1,Y1) , X1 is X center, Y1 is Y center. Method : Compute Centroid C= ( Value of rows , Value of columns ) No. of pixels No. of pixels

Experimental results: Reference Image of order 255 X 255

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

Centroid of region B Centroid = (55.75,80) Similarly we calculate Centroid for Region B Showing the centroid of the region B

Method for computing the Distance : ALGORITHM : Distance Input : (X1,Y1) & (X2,Y2) Centroid of two regions Output : Dist, distance between two regions of the reference image. Method : Compute dist = (Sqrt((X2-X1)^2), Sqrt((Y2-Y1)^2)) Now consider Centroid (X1,Y1) & (X2,Y2) and distance Dist as reference.

The Next successive Image of order 255 X 255 Experimental results:

Centroid of region B1 Centroid = (70.77,80) Showing the centroid of the region B1

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

Method for estimating the model of the cloud : Image matching : Here the region B1 is subtracted by region B and centroid of the resultant image is found out. Let (X211,Y211) be the centroid of this resultant image. This will be used in finding the shift of the region with respect to reference image. The centroid of the region B1 is found out and let it be (X21, Y21) and using this the distance between Earth(region A) and cloud(region B1).

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.

Proximity of the Cloud : Consider the Earth region and again segment the cloud above it. Calculate the percentage of cloud-covered area above Earth. The percentage of the cloud above earth region determines the proximity of the cloud. By comparing the distance of the successive images we can find whether the cloud region is moving away or towards the Earth. By comparing the shifts we can find whether the cloud movement is circular or dispersing. Thus we can give warning about condition if any adverse effect of weather condition is found.

ALGORITHM : Proximity Input : 2D array of region A, Output : Cloudpix- Percentage of cloud over earth. A message. Method : Segment the input array so that Cloud and land part get segmented. Count the number of pixels of cloud region. Subtract the resultant number of pixels from the total number of pixels of the segment and this gives the total number of pixels acquired by land. Compute tmp = total number of pixels/2. Fix this as threshold value. If(cloudpix > tmp) msg(“percentage of cloud over land is more”) Else msg(percentage of cloud over land is less). Print cloudpix.

The Ultimate goal of the project is to give warning message about the status of clouds. The Algorithm for Alarm follows MESSAGE

ALGORITHM : Alarm Input : Dist- distance between the Earth and Cloud region of the reference image. Distance1-new distance between the Earth region of reference image and Cloud region of new image. (X2,Y2)- Centroid of reference Cloud region. (X211,Y211)- Centroid of resultant image of the difference. Output : Dist3- output distance, Message or warning. Method : Compare the two distances. If (Dist3 <Dist) {print Dist3, msg (“Cloud is coming nearer to earth”)} elseif(Dist3>Dist){ print Dist3, msg (“Cloud is going away from the earth”)} If (Y211<y2) and ((X2-X211)<epsilon) {msg (“Cloud is accumulating and movement is anticlockwise”)} elseif (Y211>Y2) and ((X2-X211) < epsilon) {msg (“Cloud is accumulating and movement is clockwise”)} else if (X2-X211>epsilon) {msg (“Cloud is dispersing”).

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.

The image took after 1-hour of the previous image

Conclusions and Future work Goal of this project is early warning system-An alarm has been set up which are configurable. Dependencies on the occupancies on the value of the region we should be in the position to predict that the next successive image how the patterns are moving. Pattern is changing due to this the subsequent future work will be to interconnect the regions of interest using higher domain knowledge and other expert system tools.

Automatic Classification Satellite images for weather Monitoring

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Automatic Classification Satellite images for weather Monitoring