Quantitative Planetary Image Analysis via Machine Learning

  • Paul Tar

Student thesis: Phd


Over recent decades enormous quantities of image data have been acquired from planetary missions. High resolution imagery is available for many of the inner planets, gas giant systems, and some asteroids and comets. Yet, the scientific value of these images will only be fully realised if sufficient analytic power can be applied to their large scale and detailed interpretation. Unfortunately, the quantity of data has now surpassed researchers' abilities to manually analyse each image, whilst available automated approaches are limited in their scope and reliability. To mitigate against this citizen science projects are becoming increasingly common allowing large numbers of volunteers, using web-based resources, to assist in image interpretation. Yet human involvement, expert or otherwise, introduces additional problems of subjectivity and consistency. This thesis argues that what is required is an objective, quantitative, automated alternative.This thesis advocates a quantitative approach to making automated measurements from a range of surface features, including varied terrains and the counting of impact craters. Existing pattern recognition systems, and established practices, found within the imaging science and machine learning communities will be critically assessed with reference to strict quantitative criteria. This criteria is designed to accommodate the needs of scientists wishing to undertake quantitative research into the evolution of planetary surfaces, permitting measurements to be used with confidence. A new and unique method of pattern recognition, facilitating the meaningful interpretation of extracted information, will be presented. What makes the new system unique is the inclusion of a comprehensive predictive theory of measurement errors and additional safeguards to ensure the trustworthiness and integrity of results.The resulting supervised machine learning/pattern recognition system is applied to Monte-Carlo distributions, martian image data and citizen science lunar crater data. Conclusions are drawn that applying such quantitative techniques in practice is difficult, but possible, given appropriately encoded data and application specific extensions to theories and methods. It is also concluded that existing imaging science practices and methods would benefit from a change in ethos towards a quantitative agenda, and that planetary scientists wishing to use such methods will need to develop an understanding of their properties and limitations.
Date of Award31 Dec 2014
Original languageEnglish
Awarding Institution
  • The University of Manchester


  • statistics
  • machine learning
  • quantitative
  • planetary image analysis

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