A Robust End-to-end Deep Learning Framework for Detecting Martian Landforms with Arbitrary Orientations

With increasingly massive amounts of high-resolution images of Mars, automated detection of geological landforms on Mars has received widespread interest. It is significant for acquiring knowledge of distant planetary surfaces and processes, or manifold onboard applications such as spacecraft motion estimation and obstacle avoidance. This is a challenging task, not only because of the multiple sizes of targets and complex image backgrounds, but also the various orientations of some bar-shaped landforms in satellite images captured from the top view. The existing methods for directed landform detection require several pre or post-processing operations to extract possible regions of interest and final detection results with orientation, which are very time consuming. In this paper, a new end-to-end deep learning framework is developed for detecting arbitrarily-directed landforms. This framework, named Rotated-SSD (Single Shot MultiBox Detector, SSD), can locate and identify different landforms on Mars in one pass, by using rotatable anchor-box based mechanism. To enhance its robustness against angle variation of the targets and complex backgrounds, a new efficient match strategy is proposed for anchoring default boxes to ground truth boxes in the model training process and an autoencoder-based unsupervised pre-training operation is introduced to improve both the model training and inference performance. The proposed framework is tested for detection of bar-shaped buttes and dark slope streaks on satellite images. The detection results show that our framework can significantly contribute to onboard motion estimation systems. The comparative results demonstrate that the proposed match strategy outperforms other state-of-the-art match strategies with regard to model training efficiency and prediction accuracy. The pre-training strategy can facilitate the training of deep architectures in case of limited available training data.