This presentation discusses the mathematical morphology and applications in automated sunspot detection

Fletcher, L.

Marshall, S.

Watson, F.

English

Name not available

Mathematical morphology and applications in automated sunspot detection

University of Strathclyde Institutional Repository

Strathprints Institutional RepositoryWatson, F. and Fletcher, L. and Marshall, S. (2008) Mathematical morphology and applications inautomated sunspot detection. In: Solar Image Processing Workshop IV, 2008-10-26 - 2008-10-30,Baltimore, USA. (Unpublished)Strathprints is designed to allow users to access the research output of the University of Strathclyde.Copyright c© and Moral Rights for the papers on this site are retained by the individual authorsand/or other copyright owners. You may not engage in further distribution of the material for anyprofitmaking activities or any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or study, educational, ornot-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to Strathprints administrator:mailto:strathprints@strath.ac.ukhttp://strathprints.strath.ac.uk/  Mathematical morphology and applications in automated sunspot detectionF. Watson1, S.Marshall2 and L.Fletcher11 Department of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, U.K.Email : f.watson@astro.gla.ac.uk, l.fletcher@astro.gla.ac.uk.2 Department of Electrical and Electronic Engineering, University of Strathclyde, Glasgow, G1 1XW, U.K.Email : s.marshall@eee.strath.ac.ukMathematical morphology : the basicsMathematical morphology in image processing allows the shapes and structures in images to be used as tools in analysis. It was developed with binary images by Matheron (1975) and Serra (1982) but has been extended to include grayscale and colour. The two most basic operations are erosion and dilation. A shape called a structuring element must be chosen to probe the image with: we use a circle in this poster.ErosionRemoves protrusions from a shape or structure.Figure 1 erosionDilationFigure 2 dilationOpening and the top-hat transformationFrom the erosion and dilation operators, a number of other operators can be formed.  One of these is the opening operator (an erosion followed by a dilation). The benefit of this is that by using both basic operators, the general size of the original shape is preserved but certain regions can be removed.Sunspot detectionThe problems with detecting sunspots in an automated way are more related to the surrounding areas than the spots themselves. The spots appear clearly on images as dark regions however thresholding is not an option as the Sun itself becomes dark near the limbs. So a method which can take the local background into account was required. The top-hat transformation can provide a solution and is also the subject of work undertaken by Curto, Blanca and Martinez (2008).Figure 4 : example of the top-hat transformation. Original image on the left and processed image on the right. The example in Figure 3 gives the processed image in Figure 4. The locations of the sunspots can be clearly seen and their location and areas can be easily recorded. A major advantage of the method is speed, with a full image being processed and all spots recorded in less than 4 seconds.Curto, J.J. ; Blanca, M. ; Martinez, E. : 2008, Automatic Sunspots Detection on Full-Disk Solar Images using Mathematical Morphology. Solar Physics, 124. doi: 10.1007/s11207-008-9224-6Matheron, G.: 1975, Random sets and integral geometry, Random sets and integral geometry, by Georges Matheron, pp. 261. ISBN 0-471-57621-2, Wiley, New York.Serra, J.: 1982, Image Analysis and Mathematical Morphology, Image Analysis and Mathematical Morphology, by Jean Serra, pp. 610. ISBN 102637242X. Academic Press, London.We acknowledge the support of a Synergy partnership grant. All solar images shown are from SOHO/MDI data which is a collaboration between NASA and ESA and is a project of the Stanford-Lockheed Institute for Space Research.Figure 3 : (top left) original image inverted. (top right) original processed by erosion. (bottom left) opening of original image (erosion followed by dilation). (bottom right) subtraction of opened image from original image with spot peaks clearly visible. Intensity is measured pixel counts.DiscussionThe structuring element is rolled around the inside of the shape given by the thin black line and produces the thick black line. This set of points traced out by the centre of the disk is the erosion of the larger square with the protrusion.• Dual to erosion.• Equivalent to rolling structuring element around outside of the shape and marking centre of the circle.• Example in Figure 2.The top-hat transformation is defined as the opening of an image subtracted from the image itself and it is this that is of use in sunspot detection. Figure 3 shows an example of this using a single row of pixels from a SOHO MDI continuum image.The speed of the method allows large numbers of SOHO MDI continuum images to be analysed in a short space of time. The rate has been calculated at between 20,000 and 22,000 images per day given reasonable processing power.Accuracy of the top-hat transformation in sunspot detection was determined using a series of 'ground truth' images, dated from January 1998 to October 2002, in which a human observer had marked pixels judged to be part of a sunspot.Of the pixels marked, 77% of them were picked up by the top-hat transformation and mathematical morphology allows those areas to be used as markers to recover the whole spot area again.Also, of the pixels marked by the transformation, 4% were false positives. These were enhancements to detected spots as opposed to new spot areas being incorrectly formed. Finally, 100% of sunspots present were detected.

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Mathematical morphology and applications in automated sunspot detection

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