Data mining for surface change discovery on the lunar surface from orbital images
Lead Research Organisation:
University College London
Department Name: Mullard Space Science Laboratory
Abstract
Transient events on the lunar surface, known as impact flashes (Boulet et al., 2012), have been observed by amateur and professional astronomers, for at least the last 450 years (Buratti et al., 2000). These are believed to be primarily due to meteoritic impacts, although there is some multispectral image evidence of volcanic emissions. However, there is only very limited evidence of the origin of these flashes in orbital images (loc.cit. and Buratti & Johnson, 2003) which have been published to date. One of the reasons for this is the massive task of working through a very large number of images acquired of the Moon from sources as diverse as Apollo astronaut photography, Lunar orbiter digitised film, CLEMENTINE multispectral frame images and the more recent pushbroom images from LROC.
Unfortunately, these images are not all uniformly co-registered to any base dataset and many do not even have any georeferencing information. Under the STFC change detection using data mining project (MSSL Consolidated grant ST/K000977/1), techniques have been developed for automated co-registration, DTM retrieval and subsequent orthorectification to produce time series of sub-pixel co-registered ORIs. However, these have only been applied to orbital images of Mars.
In this project, it is planned to modify and apply these automated registration techniques to the Moon employing all the relevant image data from the 1960s to the present-day very high resolution (<20cm) image data of the Moon from Japanese, Indian, Chinese and US sensors.
Data mining techniques will be employed to search for changes in lunar images after applying image normalisation to take into account differences in the viewing and illumination conditions and their interaction with the surface. These data mining results will also be validated using data from the MoonZoo project, where available, to assess which is the best source. Any changes detected will then be studied using the vast panoply of spectral and directional measurements available from orbit today in addition to the generation of the highest possible DTMs. Automated crater detection software, developed by the incoming student during his MSc Space Science dissertation, will be used to map large regions where new craters have been detected to determine what effect such random impacts have on the age dating using crater chronology methods developed by Neukum (1983,1984)
Cited references
Bouley et al. (2012) Power and duration of impact flashes on the Moon: Implication for the cause of radiation. Icarus, vol. 218 pp. 115-124
Buratti et al. (2000) Lunar Transient Phenomena: What Do the Clementine Images Reveal? Icarus, vol. 146 pp. 98-117
Buratti and Johnson. (2013) Identification of the lunar flash of 1953 with a fresh crater on the moon's surface. Icarus, vol. 161 pp. 192-197
Neukum, G.(1984) Meteorite bombardment and dating of planetary surfaces. Thesis - Feb. 1983. NASA Technical Memorandum TM-77558 (English translation of German thesis published originally in February 1983), pp. 1-158.
Unfortunately, these images are not all uniformly co-registered to any base dataset and many do not even have any georeferencing information. Under the STFC change detection using data mining project (MSSL Consolidated grant ST/K000977/1), techniques have been developed for automated co-registration, DTM retrieval and subsequent orthorectification to produce time series of sub-pixel co-registered ORIs. However, these have only been applied to orbital images of Mars.
In this project, it is planned to modify and apply these automated registration techniques to the Moon employing all the relevant image data from the 1960s to the present-day very high resolution (<20cm) image data of the Moon from Japanese, Indian, Chinese and US sensors.
Data mining techniques will be employed to search for changes in lunar images after applying image normalisation to take into account differences in the viewing and illumination conditions and their interaction with the surface. These data mining results will also be validated using data from the MoonZoo project, where available, to assess which is the best source. Any changes detected will then be studied using the vast panoply of spectral and directional measurements available from orbit today in addition to the generation of the highest possible DTMs. Automated crater detection software, developed by the incoming student during his MSc Space Science dissertation, will be used to map large regions where new craters have been detected to determine what effect such random impacts have on the age dating using crater chronology methods developed by Neukum (1983,1984)
Cited references
Bouley et al. (2012) Power and duration of impact flashes on the Moon: Implication for the cause of radiation. Icarus, vol. 218 pp. 115-124
Buratti et al. (2000) Lunar Transient Phenomena: What Do the Clementine Images Reveal? Icarus, vol. 146 pp. 98-117
Buratti and Johnson. (2013) Identification of the lunar flash of 1953 with a fresh crater on the moon's surface. Icarus, vol. 161 pp. 192-197
Neukum, G.(1984) Meteorite bombardment and dating of planetary surfaces. Thesis - Feb. 1983. NASA Technical Memorandum TM-77558 (English translation of German thesis published originally in February 1983), pp. 1-158.
Publications
Francis A
(2020)
A Multi-Annotator Survey of Sub-km Craters on Mars
in Data
Francis A
(2019)
CloudFCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning
in Remote Sensing
Francis A
(2022)
SEnSeI: A Deep Learning Module for Creating Sensor Independent Cloud Masks
in IEEE Transactions on Geoscience and Remote Sensing
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/R505171/1 | 01/10/2017 | 24/12/2021 | |||
1912521 | Studentship | ST/R505171/1 | 06/11/2017 | 05/08/2021 | Alistair Francis |
Description | Visiting Researcher position at ESA's Phi Lab |
Organisation | European Space Agency |
Department | Centre for Earth Observation |
Country | Italy |
Sector | Charity/Non Profit |
PI Contribution | Worked on the ESRIN site within the Phi Lab team on computer vision models. |
Collaborator Contribution | Their computing resources, technical support, domain expertise and networking capability. |
Impact | Forthcoming labelled dataset of Sentinel-2 imagery Forthcoming publication on deep learning model |
Start Year | 2019 |
Title | CloudFCN |
Description | Novel deep learning cloud masking algorithm written in Python. Designed for use on Landsat 8 dataset, with scripts for experiments as conducted in the related paper: https://doi.org/10.3390/rs11192312 |
Type Of Technology | Software |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | User-friendly deep learning model for cloud masking in Landsat 8 |
URL | https://github.com/aliFrancis/cloudFCN |