Morphological Feature Extraction for Statistical Learning With Applications To Solar Image Data

Many areas of science are generating large volumes of digital image data. In order to take full advantage of the high-resolution and high-cadence images modern technology is producing, methods to automatically process and analyze large batches of such images are needed. This involves reducing complex images to simple representations such as binary sketches or numerical summaries that capture embedded scientific information. Using techniques derived from mathematical morphology, we demonstrate how to reduce solar images into simple 'sketch' representations and numerical summaries that can be used for statistical learning. We demonstrate our general techniques on two specific examples: classifying sunspot groups and recognizing coronal loop structures. Our methodology reproduces manual classifications at an overall rate of 90% on a set of 119 magnetogram and white light images of sunspot groups. We also show that our methodology is competitive with other automated algorithms at producing coronal loop tracings and demonstrate robustness through noise simulations. (C) 2013 Wiley Periodicals, Inc.