Mapping Cones on Mars in High-Resolution Planetary Images with Deep Learning-Based Instance Segmentation

Cones are among the significant and controversial landforms on Mars. Martian cones exhibit various morphological characteristics owing to their complex origin, and their precise origin remains an active research topic. A limited number of cones have been manually mapped from high-resolution images in local areas, and existing detection methods are only applicable to a single type of cone that has a similar morphology and spatial distribution, leading to the vast majority remaining unidentified. In this paper, a novel cone identification approach is proposed that is specially designed for adequately recognizing cones from different regions in high-resolution planetary images. First, due to the lack of a publicly available cone database for reference, we annotated 3681 cones according to the literature on manual interpretation and the cone information provided by the Lunar and Planetary Laboratory (IRL) in HiRISE images. Then, the cone identification problem was converted into an instance segmentation task, i.e., a cone identification approach was designed based on deep neural networks. The Feature Pyramid Network-equipped Mask R-CNN was utilized as the detection and segmentation model. Extensive experiments were conducted for fine recognition of Martian cones with HiRISE. The results show that the proposed approach achieves high performance; it especially efficiently detects multiple types of cones while generating accurate segmentation to describe the geometry contour of cones. Finally, a Martian cone dataset with deep learning-based instance segmentation (DL-MCD) was built, containing 3861 cones for exploring geological processes on the surface of Mars.