Cross-Modality Adaptive Feature Fusion for Multitype and Multiscale Impact Craters Identification on Mars

Abstract

Craters are the most typical geologic structures and landforms on the surface of Mars. Martian craters are widely distributed in a variety of morphology with multiple types and exhibit significant differences in scale. Many attempts have been made to automatic identification of Martian craters, yet existing methods do not satisfy the need for large-scale identification. In this article, we first integrate a Martian crater dataset related to the mid- and low-latitude regions, which contains different types and various scales of craters. Then, a dual convolutional neural network (CNN)-Transformer-based cross-modality adaptive feature fusion network (DCT-CMAFFNet) is proposed for accurate identification of the multitype and multiscale craters at large scale on the Martian surface. The proposed network takes full advantage of the rich morphological features contained in Martian imagery and the topographical information reflected by the digital elevation model (DEM) data. It contains two modules: one is the dual CNN-Transformer part, which employs a hybrid architecture to extract the local detailed and global deep features of Martian craters from images and DEM and the other is CMAFF module, which exploits self-attention mechanism to learn the relationship between the images and DEM modalities and weigh each position of the deep feature maps to ensure a comprehensive identification of multitype and multiscale Martian craters. By adaptively fusing the rich information from imagery and DEM, the proposed network identified 3166 new Martian impact craters larger than 1 km, achieving a 14%–24% improvement in accuracy compared to methods using either a single data source or data feature fusion modality.