Automatic Change Detection of Planetary Striped Landform on Mars Surface

Cameras carried by satellites take large-scale images of the planetary surface, which provides data support for exploration missions. With the progress of deep space exploration, the demands to identify surface changes are gradually increasing. There are few studies that focus on planetary surface automatic change detection since it faces the challenges of difficult registration, strong pseudochange, poor automatic detection capacity, etc. To handle these challenges, we propose a deep learning lightweight model SLCD-Net (i.e., striped landform change detection model) for patchwise change detection of the planetary striped landform. SLCD-Net uses the dual-input-based siamese network as the overall architecture to learn the deep semantic information of pre- and posttemporal images. Importantly, SLCD-Net designs a dual-feature multilevel complementary fusion module between two branches of the siamese network, which can learn the cross-temporal complementary features and eliminate the noise impact. Besides, SLCD-Net exploits a spatial attention module after the fusion unit, which further helps to weaken the noise that is produced by fusion. Furthermore, we construct a dataset about dark slope streak (DSS, a striped landform type). Training on our DSS dataset enables SLCD-Net to have an advantage in identifying the variation of DSS. The SLCD-Net's detection performance on multiple planetary surface datasets including our DSS test set proves that it has achieved a breakthrough in robustness and generalization. Not limited to the patchwise task, the change map generated by SLCD-Net applied to different regions also proves that it can be effectively used for pixelwise Mars striped landform change detection.