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Automated Classification of Sunspot Groups with Support Vector Machines Mehmmood A. Abd Electrical And Computer Engineering Department, University Of Windsor, ON, Canada

Sarab F. Majed Mathematic Department, University Of Western Ontario, ON, Canada

V. Zharkova

Department of Computing and Mathematics, Bradford University, UK.

Abstract__A new effective technique is presented for automatic classification of sunspot groups on full disk white light (WL) solar images. This technique is implemented on images taken from the Solar Oscillations Investigation Michelson Doppler image (SOI/MDI) aboard the Solar Heliospheric observatory (SOHO). The technique focuses on employing Support Vector Machines (SVMs) as effective classification tool. In addition to applying SVMs the problem of extracting sunspots and sunspot groups from solar image is solved in an efficient and different way from the ones previously employed. This technique proceeds in several consequence phases. The first phase involves solar disk image extracting. The second phase involves Binarization and smoothing of an extracted solar image disk. The third phase involves unsupervised segmentation of sunspots groups. This phase consists of two subphases: a) extracting the spots and, b) combining together the sunspots that belong to the same group. The fourth phase involves attributes extraction of each sunspots group. The final phase is the classification phase using SVMs, by applying a one–against–all technique to classify the sunspots groups. The proposed technique has been tested using different sets of sunspots achieving approximately 88.56% recognition rate for Zurich classification of sunspot groups. Index Term__Solar Disk, Sunspots Groups, Support Vector Machines, Unsupervised Segmentation.

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I. INTRODUCTION

unspots have been a subject of interest to scientists and researchers for many years. A sunspot is a region in the sun’s photosphere that is darker by comparison with its photosphere background and has intense magnetic field. A sunspot may consist of one or more umbra which often are surrounded by a less dark area called penumbra. Sunspot numbers increase and decrease with an irregular cycle with a length of approximately 11 years [1]. Sunspots can be observed in the visible continuous spectrum, also known as “ white light ‘. Larger sunspots can be observed by using Call K1 absorption line image and H and Call K3 absorption line images . Sunspot shapes are varied from individual spots to a

group of spots. Sunspot groups can have infinite different formations and size, ranging from solo spots to the giant groups of sunspots with complex structures [1]. Despite such complexity and diversity, astronomers have been able to classify the sunspots classes based on The McIntosh Sunspot Classification Scheme and modified Zurich Scheme [2]. A sunspot is recognized by its two main features ; the central dark features ‘umbra’; and the outer lighter area which is called ‘penumbra’ (See Figure 1).Therefore,most computerized recognition techniques rely on these properties . Many manual sunsopts in different formats are produced all over the world , such as the Meudon Observatory in France ,the Locarno Solar Observatory in Switzerland ,the Mmount Wilson Observatory in the United States Of America and many others . Zurich classification is based on the Locarno Catalogue which is considered a primary indicator of solar activity [2][3]. The Zurich Classification is categories into seven classes of sunspots groups numbered from A to H [2] .(see Figure 2 and Table 1 ). Because of the effect of the solar activity on the Earth’s atmosphere, human activity, businesses and even climate, the automated detection and verification techniques become essential for a reliable forecast of the future effects on human lives and businesses. This requires stringent requirments on the classification accuracy of any automated recognition system. Many researchers have been developing automated reliable recognition systems with varying recognition rate and techniques [3].

Fig.. 1. A high-resolution SOHO/MDI image showing sunspots with umbra (black spots) and penumbra (dark gray) above the background (gray).

321 K. Elleithy et al. (eds.), Technological Developments in Networking, Education and Automation, DOI 10.1007/978-90-481-9151-2_56, © Springer Science+Business Media B.V. 2010

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ABD ET AL. TABLE 1 SUNSPOT CLASS DESCRIPTION

The remainder of this paper is organized as follows. Section II describes preprocessing, Section III presents the segmentation technique, Section IV presents feature extraction technique, Section V presents multi-class SVMs Classification technique, Section VI discusses the results obtained from the previous sections and summarizes the conclusions . II. PREPROCESSING Preprocessing consists of image acquisitions, edge detection, noise removing and feature enhancing, and Binarization. The input images are taken from the solar Oscillations Investigation Michelson Doppler Imager (SOI/MDI) aboard of the solar Heliospheric Observatory (SOHO)[11]. Figure 3 shows an example of the input white light full disc image .The image has dimensions of 1024x1024 pixels with 16 different intensities. The resolution is 2.6 arces/pixel. Edge detection, noise removing and Binarization are curried out as that reported by Sarab et al [4].

In this paper a new recognition technique is presented based using one–against-all SVMs. The classification system receives its input from reliable segmentation phase that employed the unsupervised segmentation method [4]. The objectives of this system is to a) improve the classification rate of sunspot groups classification,b) improve segmentation techniques of sunspots groups, c) employ more reliable attributes to enhance the classification reliability. The proposed methodology is described by the following processing phases : 1. Preprocessing 2. Segmentation technique 3. Feature extraction 4. Multi–Class SVMs Classification

Fig. 3. Full disk image obtained from SOHO/MID on 1/1/2000.

Image binarization converts an image into black and white. In this study a threshold value, t, is determined automatically using Otsu’s method [12] .The produced image is a binary image with white sunspots and black background. Figure 4 illustrates the output that will be feed to the next phase /segmentation phase.

Fig.2. The Modified Zurich Sunspot Classification System developed by Patrick McIntosh [2].

Morphological processing facilitates the sunspot groups’ attributes extracting. Meaningful shape information of geometrical objects, measuring typical characteristics area, bounding box, centroid coordinate, and the length of a perimeter, is computed. Finding sunspot coordinates (x,y) and area in pixels ,eases a determination of the sunspots locations in relation to the solar center . Moreover, area of any sunspot is utilized to estimate the area in heliographic coordinates. These attributes are employed later to classify each group belong to which class. Next section presents sunspots/ sunspot groups detection and grouping then extracting.

AUTOMATED CLASSIFICATION OF SUNSPOT GROUPS III. SEGMENTATION TECHNIQUE In this paper an unsupervised segmentation method was employed based on the similarity measure. The sunspot’s is extracted /cut from a full disk solar disk image by computing the bounding rectangle (box) that contains it.

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four different sunspot group images where A represents the pores and groups B, C, and D respectively [2]. IV. FEATURE EXTRACTION In this phase the attributes of each sunspots groups are extracted and formed into a vector of N (7 x 1) based on the Modified Zurich classification attributes. After extracting all the attributes the vector is fed to the SVMs classifier [6], [5]. The features consist of a number of sunspots/umbra, size, penumbra, penumbra’s position, bipolar, unipolar, and major longitudinal axis. The seven classes of modified Zurich classification are used as standard classes in this paper. For further details see [2]. The classes are numbered as A, B, C, D, E, F, and H. To make the classification more reliable the class letters are converted to numbers from 1 to 7. Thus, the class A is represented by one (1) and B by (2) and so on. The attributes of each sunspot or sunspots groups are formed into vector and fed to the SVMs. Multi-class SVMs are employed in this paper.

Fig. 4. Black and white image produced from the preprocessing phase.

Then an iterative algorithm is employed to estimate the maximum bounding box that will be re-adjusted iteratively, in order to combine the group of sunspots together. The images used are WL of size Nr x Nc with intensities given S(i,j) ={Sij: 1