Mathematical Morphology is an image processing tool with the capacity of filtering the image, remove unwanted features, or highlighting important features. It can be applied to almost any field that requires image processing. Some of the fields where it’s applied are: medicine, civil engineering, applications based on satellite data, urban planning, disaster management, among others.

In the context of space weather applications, mathematical morphology is particularly useful for processing and analyzing data from various imaging instruments that monitor space phenomena. In particular, mathematical morphology can be useful in detecting the sun’s active regions and particular events that happen in these regions or around them, such as sunspots, solar flares, and CMEs (coronal mass ejections), all of these important for space weather monitoring and forecasting. Like a shape discovery bucket for children, with which children can become acquainted with and learn shapes,  we can think of mathematical morphology as a method where we identify particular features/events in an image, more specifically, in the Sun, by fitting them in a particular morphological model.

Some of the benefits of this method are its broad applicability to many different types of image data, its efficiency in highlighting important features, even if they have complex or irregular shapes, and its flexible customization.

If you want to find in more detail how this method works, how it is used in space weather monitoring and forecasting (and many other applications), and how it can work in harmony with other technologies like AI, check Slava Bourgeois’ webinar via: Space Webinar – Exploring Solar Dynamics with Morphological Algorithms – YouTube