Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream

An assessment of different relay network topologies to improve Earth-Mars communications

The future of deep space communications encompasses a challenging situation where the current facilities used to communicate with different spacecraft may become saturated as a result of an increasing number of missions and their complexity. From this forecast, the present study intends to provide a solution to saturation problems through strategically-located upgradable relays for Earth-Mars communications. The foremost goal of this paper is to quantitatively uncover the potential enhancements coming from relay placement in strategic orbits between Earth and Mars. Herein, two relay configurations –a.k.a. network topologies– are analyzed: the Lagrange-relays network topology and a circular, homogeneously-distributed satellite constellation, acknowledged here as pearl constellation. The first uses the Earth-Sun system Lagrange points L3, L4 and L5 as potential locations for the relays, whilst the second defines an optimized orbit between Earth and Mars with 3 or 4 relay satellites. To aid in the analysis, the authors developed an open-sourced piece of software that obtains the link availability as well as the data rate at which two nodes may communicate, taking as a reference the Deep Space Network for Earth, and the Mars Reconnaissance Orbiter for Mars. For complex topologies with more than two communicating nodes, the software outputs the end-to-end bit rate and optimal communication route at each time step. Moreover, this product is extensible to analyze and optimize any network topology and could be adapted to be used for contact management and mission planning in the future. The results show that the network-topology proposals are an advantageous option to significantly increase the link availability of Earth-Mars communications. Nevertheless, the Direct-To-Earth link always outperforms the multi-hop path due to the limited telecommunication system’s capabilities of both the spacecraft and the relays. As a result of this, the study includes an analysis on the requirements of the relay’s design in order to make the constellation a beneficial and comparable alternative to the DTE link. This way, the proposed network topologies become a suitable option whom to share with the DSN communications workload, providing enhanced bit rates and data volumes as well as higher availability of the communication.Peer ReviewedPostprint (published version