## 1 Introduction
As a pixel-based method, the AMET model cannot obtain a complete auroral oval in the low contrast region. Though the MSRM method can segment the auroral oval region completely, the segmentation result does not agree well with the manually annotated benchmark. The LLSRHT and FCM + QEF models applied the elliptic or quasi-elliptic fitting to extract auroral oval boundaries. Therefore, the segmentation results obtained by these two methods often have smooth inner and outer boundaries, which is inconsistent with the fact that the boundaries of some auroral oval are rugged. Figure 1: Aurora images. (**a**) Aurora images captured by ultraviolet imager (UVI). (**b**) Aurora images captured by all-sky imagers (ASI). Over the past few years, the active contour model (ACM) proposed by [PERSON] et al. [21] has been widely perused and applied to image segmentation. The fundamental idea of the ACM is to control the curve to evolve towards its interior normal and stop on the boundary of an object based on the energy minimization model [22]. Relying on the local spatial distance within a local window, [PERSON] et al. introduced a region-based active contour model via local similarity factor (RLSF) to improve the segmentation results of images with unknown noise distribution [23]. Based on an improved signed pressure force (SPF) and local image fitting (LIF), [PERSON] et al. proposed the improved SPF- and LIF-based image segmentation (SPFLIF-IS) algorithm to address the difficulty that the inhomogeneous image cannot be segmented quickly and accurately [24]. Moreover, some researchers refine the active contour model for auroral oval segmentation. For instance, [PERSON] et al. introduced the interval type-2 fuzzy sets (IT2 FS) into the active contour model to obtain a more accurate segmentation result [11]. As a widely investigated active contour model that can address the difficulties related to topological changes during the evolution process, level set methods have been introduced into the auroral oval extraction in recent years. [PERSON] [25] proposed a shape-initialized and intensity-adaptive level set method (SIIALSM) to extract auroral oval boundaries. Research by [PERSON] [26] utilized the distance constraint of inner and outer boundaries in the local information based dual level set method (LIDLSM) to determine the auroral oval regions. However, these two level set based methods are sensitive to the image inhomogeneity and evolving curve initializations. If there exist low contrast regions in the UVI images or the initial contour is set improperly, the auroral oval cannot be extracted accurately by these two methods. In recent years, owing to the rapid development of deep learning techniques, we have witnessed a lot of groundbreaking results [27; 28; 29]. As the most common used network in deep learning, the convolutional neural network (CNN) has been widely applied to computer vision tasks and gains remarkable popularity. In this work, the CNN based global energy term and adaptive time-step are proposed to address the segmentation failure brought by image inhomogeneity and different contour initializations. The flow diagram of our work is shown in Figure 2. First, the CNN designed in our work is trained on the training samples extracted from the UVI image data set. Following this, the global energy term and adaptive time step are devised based on the confidence value obtained from trained CNN. Subsequently, the global energy term and adaptive time-step are introduced into the LIDLSM for the extraction of the auroral oval. Experiments performed on the UVI image data sets demonstrate that the proposed method, compared with the state-of-the-art algorithms, can obtain more accurate segmentation results and is more robust to different contour initializations. Figure 2: The flow diagram of the deep feature-based adaptive level set (DFALS) model. The remainder of the paper is organized as follows.