Enhancement classification of galaxy images
With the advent of astronomical imaging technology developments, and the increased capacity of digital storage, the production of photographic atlases of the night sky have begun to generate volumes of data which need to be processed autonomously. As part of the Tonantzintla Digital Sky Survey construction, the present work involves software development for the digital image processing of astronomical images, in particular operations that preface feature extraction and classification. Recognition of galaxies in these images is the primary objective of the present work. Many galaxy images have poor resolution or contain faint galaxy features, resulting in the misclassification of galaxies. An enhancement of these images by the method of the Heap transform is proposed, and experimental results are provided which demonstrate the image enhancement to improve the presence of faint galaxy features thereby improving classification accuracy. The feature extraction was performed using morphological features that have been widely used in previous automated galaxy investigations. Principal component analysis was applied to the original and enhanced data sets for a performance comparison between the original and reduced features spaces. Classification was performed by the Support Vector Machine learning algorithm.