Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.Comment: 10 pages (expanded citations compared to 8 page submitted version for decadal survey), 3 figures, white paper submitted to the Planetary Science and Astrobiology Decadal Survey 2023-203

Visualizing Image Content to Explain Novel Image Discovery

The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends. We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the data set or, potentially, the discovery of previously unknown phenomena. To aid in interpreting and diagnosing the novel aspect of these selected observations, we recommend the use of novelty detection methods that generate explanations. In the context of large image data sets, these explanations should highlight what aspect of a given image is new (color, shape, texture, content) in a human-comprehensible form. We propose DEMUD-VIS, the first method for providing visual explanations of novel image content by employing a convolutional neural network (CNN) to extract image features, a method that uses reconstruction error to detect novel content, and an up-convolutional network to convert CNN feature representations back into image space. We demonstrate this approach on diverse images from ImageNet, freshwater streams, and the surface of Mars.Comment: Under Revie

Novelty Detection for Multispectral Images with Application to Planetary Exploration

In this work, we present a system based on convolutional autoencoders for detecting novel features in multispectral images. We introduce SAMMIE: Selections based on Autoencoder Modeling of Multispectral Image Expectations. Previous work using autoencoders employed the scalar reconstruction error to classify new images as novel or typical. We show that a spatial-spectral error map can enable both accurate classification of novelty in multispectral images as well as human-comprehensible explanations of the detection. We apply our methodology to the detection of novel geologic features in multispectral images of the Martian surface collected by the Mastcam imaging system on the Mars Science Laboratory Curiosity rover

Mars in the Visible to Near Infrared: Two Views of the Red Planet

abstract: Remote sensing in visible to near-infrared wavelengths is an important tool for identifying and understanding compositional differences on planetary surfaces. Electronic transitions produce broad absorption bands that are often due to the presence of iron cations in crystalline mineral structures or amorphous phases. Mars’ iron-rich and variably oxidized surface provides an ideal environment for detecting spectral variations that can be related to differences in surface dust cover or the composition of the underlying bedrock. Several imaging cameras sent to Mars include the capability to selectively filter incoming light to discriminate between surface materials. At the coarse spatial resolution provided by the wide-angle Mars Color Imager (MARCI) camera aboard the Mars Reconnaissance Orbiter (MRO), regional scale differences in reflectance at all wavelengths are dominated by the presence or absence of Fe3+-rich dust. The dust cover in many regions is highly variable, often with strong seasonal dependence although major storm events can redistribute dust in ways that significantly alter the albedo of large-scale regions outside of the normal annual cycle. Surface dust reservoirs represent an important part of the martian climate system and may play a critical role in the growth of regional dust storms to planet-wide scales. Detailed investigation of seasonal and secular changes permitted by repeated MARCI imaging coverage have allowed the surface dust coverage of the planet at large to be described and have revealed multiannual replenishing of regions historically associated with the growth of storms. From the ground, rover-based multispectral imaging acquired by the Mastcam cameras allows compositional discrimination between bedrock units and float material encountered along the Curiosity rover’s traverse across crater floor and lower Mt. Sharp units. Mastcam spectra indicate differences in primary mineralogy, the presence of iron-bearing alteration phases, and variations in iron oxidation state, which occur at specific locations along the rover’s traverse. These changes represent differences in the primary depositional environment and the action of later alteration by fluids circulating through fractures in the bedrock. Loose float rocks sample materials brought into the crater by fluvial or other processes. Mastcam observations provide important constraints on the geologic history of the Gale Crater site.Dissertation/ThesisSupplemental Animations for Chapter 2Doctoral Dissertation Geological Sciences 201