A crater region detection algorithm based on automatic feature learning

The crater-based navigation technology has been become a novel and precise autonomous navigation method in space exploration, and how to extract the crater regions from the crater navigation image is the essential condition of the crater-based navigation method. Accordingly, in this paper, we propose an algorithm for extracting crater regions via automatic feature learning. First, the candidate crater regions were obtained by the maximal stable external region method. Then, the features of these regions were automatically extracted by Convolutional Neural Network (CNN). Finally, the true crater regions were identified from all the candidate regions through Support Vector Machine (SVM) classifier. The experimental results demonstrate that the proposed algorithm can extract crater regions from the navigation image with higher accuracy and robustness than the traditional crater region detection algorithms based on the handcrafted features. The proposed algorithm obtains an F1 score which is 8% higher than that of the traditional method on the standard Mars surface crater database, and can be applied in the crater detection of the crater-based visual navigation method to provide the precise navigation landmarks. © 2021, Editorial Board of JBUAA. All right reserved.