A multi-channel approach for segmentation of solar corona in images from the solar dynamics observatory

We present a multi-channel segmentation scheme to identify different features of the solar corona, such as coronal holes, active regions and the quiet sun (especially in the ultraviolet and extreme ultraviolet images). In contrast to common techniques, we use an approach that uses image intensity and relative contribution of each of the wavelengths. This approach is illustrated by using the images taken by the AIA telescopes onboard of the SDO mission. This technique incorporates a nearest-neighbor based classifier followed by Moore-neighbor tracing algorithm to find the boundaries and track the regions of interest. This method requires less computation time as compared to the commonly used fuzzy logic methods and is robust in the sense it performs equally well in both the central and limb regions of the solar disc.