Aurora image segmentation by combining patch and texture thresholding

The proportion of aurora to the field-of-view in temporal series of all-sky images is an important index to investigate the evolvement of aurora. To obtain such an index, a crucial phase is to segment the aurora from the background of sky. A new aurora segmentation approach, including a feature extraction method and the segmentation algorithm, is presented in this paper. The proposed feature extraction method, called adaptive local binary patterns (ALBP), selects the frequently occurred patterns to construct the main pattern set, which avoids using the same pattern set to describe different texture structures in traditional local binary patterns. According to the different morphologies and different semantics of aurora, the segmentation algorithm is designed into two parts, texture part segmentation based on ALBP features and patch part segmentation based on modified Otsu method. As it is simple and efficient, our implementation is suitable for large-scale datasets. The experiments exhibited the segmentation effect of the proposed method is satisfactory from human visual aspect and segmentation accuracy.

Research highlights
► Segment aurora from background of sky.
► Adaptive local binary patterns (ALBP) selects the frequently occurred patterns to construct the main pattern set, which avoids using the same pattern set to describe different texture structures in traditional local binary patterns.
► Two parts, texture part segmentation based on ALBP features and patch part segmentation based on modified Otsu method.
► Simple, efficient, suitable for large-scale datasets.