Abstract
Abstract. In this paper, a novel automatic crater detection algorithm (CDA) based on traditional texture feature and random projection depth function has been proposed. By using traditional texture feature, mathematical morphology is used to identify crater initially. To further reduce the false detection rate, random projection depth function is used. For this purpose, firstly, gray level co-occurrence matrix and a novel grade level co-occurrence matrix are both used to further obtain the texture features of these candidate craters. Secondly, based on the above collected features, random projection depth function is used to refine the crater candidate detection results. LRO Narrow Angle Camera (NAC) mosaic images (1 m/pixel) and Wide-angle Camera (WAC) mosaic images (100 m/pixel) are used to test the accuracy of proposed method. The experimental results indicate our proposed method is robust to detect craters located in different terrains.
Highlights
Craters have been used as important landmarks for high-precise landing of lander, autonomous spacecraft and rover navigation and control (Yu et al, 2014; Wang et al, 2015)
Craters play an important role in the study of planets chronology (Barlow, 2015)
The size frequency distribution of primary craters can provide the primary mechanism for establishing chronology of planetary surfaces (Head et al, 2010)
Summary
Craters have been used as important landmarks for high-precise landing of lander, autonomous spacecraft and rover navigation and control (Yu et al, 2014; Wang et al, 2015). With the continuous development of computer vision technology, machine learning techniques are widely used (Lienhart et al, 2002; Ratsch et al, 2001), including support vector machines (SVMs) (Suykens et al, 1999), boosting approach (Bandeira et al, 2012), decision tree (Mishra et al, 2013) and CNNs (Palafox et al, 2017). These methods do not rely on expert’s domain knowledge, but depend on learning the features based on training samples, which are more efficient for detecting multi-scale caters. These methods request a lot of labelled data for training and their performance depends on the quality and number of training data, in addition, the model parameters are complex
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