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Remote Sens. 2018, 10(7), 1067; https://doi.org/10.3390/rs10071067

CraterIDNet: An End-to-End Fully Convolutional Neural Network for Crater Detection and Identification in Remotely Sensed Planetary Images

Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, No.37 Xueyuan Road, Haidian District, Beijing 100191, China
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Received: 23 May 2018 / Revised: 29 June 2018 / Accepted: 3 July 2018 / Published: 5 July 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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Abstract

The detection and identification of impact craters on a planetary surface are crucially important for planetary studies and autonomous navigation. Crater detection refers to finding craters in a given image, whereas identification means to actually mapping them to particular reference craters. However, no method is available for simultaneously detecting and identifying craters with sufficient accuracy and robustness. Thus, this study proposes a novel end-to-end fully convolutional neural network (CNN), namely, CraterIDNet, which takes remotely sensed planetary images of any size as input and outputs detected crater positions, apparent diameters, and identification results. CraterIDNet comprises two pipelines, namely, crater detection pipeline (CDP) and crater identification pipeline (CIP). First, we propose a pre-trained model with high generalization performance for transfer learning. Then, anchor scale optimization and anchor density adjustment are proposed for CDP. In addition, multi-scale impact craters are detected simultaneously by using different feature maps with multi-scale receptive fields. These strategies considerably improve the detection performance of small craters. Furthermore, a grid pattern layer is proposed to generate grid patterns with rotation and scale invariance for CIP. The grid pattern integrates the distribution and scale information of nearby craters, which will remarkably improve identification robustness when combined with the CNN framework. We comprehensively evaluate CraterIDNet and present state-of-the-art crater detection and identification performance with a small network architecture (4 MB). View Full-Text
Keywords: crater detection; crater identification; convolutional neural network; remotely sensed image crater detection; crater identification; convolutional neural network; remotely sensed image
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Wang, H.; Jiang, J.; Zhang, G. CraterIDNet: An End-to-End Fully Convolutional Neural Network for Crater Detection and Identification in Remotely Sensed Planetary Images. Remote Sens. 2018, 10, 1067.

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