Autonomous Martian rock image classification based on transfer deep learning methods

In Mars exploration, rocks are good targets for compositional analysis with spectrometers. Their shape, size, and texture could provide a wealth of information for study of planetary geology. However, imitations on communications between Mars and earth lead to operations latencies and slow progress in planetary surface missions. Increasing the autonomy of rovers has become an important research direction. Autonomy is the ability to choose which scientific data to collect and which ones to send back to Earth. One of the aims is to recognize the rocks independently. The AEGIS system adopts the method of edge detection to select potential rock targets for following observation, but the type of rocks cannot be distinguished. Convolutional neural network (CNN) is getting more attention due to its performance in computer vision. However, a common issue of CNN is that it requires large amount of rock images for training, which are difficult to get. Transfer learning provides a good way to overcome the problem of lack of dataset. In this work, CNN based on vgg-16 architecture with deep transfer learning is used to automatically classify 4 groups of Martian rocks. The proposed model achieves accuracy of 100% on Martian rock images we collected from MSL Analyst 's Notebook. Moreover, a comparison between the VGG-16 transfer model and other models is made, and it can be found that the proposed model has the best performance in Martian rock classification.