Grants and Contributions

CAMDAS Earth Observation Data Exploitation Tool Development

SSCL, along with its project partners Maerospace Corporation, 4DM, and Planet is conducting research to validate a concept for an advanced Maritime Doman Awareness (MDA) system. This system exploits the explosion of commercial earth observations satellites and advanced data analytics to establishes persistent, global MDA. Planet designs, builds and operates the world's most capable constellation of earth-imaging satellites and they are providing access to a rich dataset that will be used to evolve the tools and techniques to advance CAMDAS to a fully operational system.

Title: Utilization of Multi Radar Data Sets with Advanced Data Fusion Techniques. Exploitation of RADAR data sets using InSAR technology to measure heave and subsidence has been proven but frequency and resolution differences between the satellites has meant that the archives have always been processed separately and could not be combined, thus limiting the potential to track change over the life of multi missions. The objective of this project is to combine ERS, Envisat, R1/R2, Sentinel and X-band data in the InSAR domain.

Title: Development and Demonstration of Capability to re-Project and Merge Optical Data to Fully Calibrated Slant Range SAR. Application specialists who wish to combine radar and optical data face the dilemma of deciding how to co-register their data prior to merging or integration. The traditional approach to merging radar data with optical data is to convert the radar data to a nominal projection, followed by a second correction for topographic effects. This approach has a number of disadvantages. The main drawback is caused by the modification of the original data due to resampling and re-projection. In this proposal, we propose a methodology by which SAR data can be calibrated for slope effects, followed by a mapping of the optical data into the geometry of the SAR data. This procedure enables the combination of optical data with unaltered SAR data, in the belief that merging optical data with radiometrically scaled and phase preserved SAR data will significantly improve classification results.

McMaster University will develop the NEUDOSE CubeSat. It will test an instrument that may offer new ways to measure the amount of radiation to which astronauts could be exposed during spacewalks, as it poses serious risks to their health. This instrument has the potential to replace current dosimetry equipment on board the International Space Station. It's measurements will be used to identify high dose rate areas and incoming solar storms, and to improve radiation modelling tools in preparation for future deep-space missions.

NEUDOSE will be developed in collaboration with Ontario-based Mohawk College, as well as Bubble Technology Industries Inc. and NASA Goddard Space Flight Center in the USA.

The ESSENCE mission will test a Canadian-developed wide-angle camera to observe snow and ice coverage in Northern Canada. The information collected through its images could help map the thawing of Arctic ice and permafrost and give a better picture of the impacts of climate change in the region. It will additionally test a solar energetic proton detector, provided by the University of Sydney, to collect data that will enable a better understanding of the effect of Solar Proton Events (SPEs). SPEs are the result of solar activity during which radioactive protons emitted by the Sun become highly energized. The level of radioactivity caused by such events can penetrate and cause damage to the structure and electronic components of spacecraft in their paths. Understanding these events and their effects could help improve the design of CubeSats so they are more resistant to radiation.

The CubeSat will be developed in collaboration with Canadensys Aerospace, ICT Seneca College of Applied Arts and Technology and the University of Sydney in Australia.

The overarching goal of this study is to improve crop type identification and biophysical parameters estimation in Canada by integrating RADARSAT-2 and other sources of data with ecosystem modelling.

The OneWeb satellite constellation will bring affordable broadband Internet access to the world. The satellites are expected to spend five years in operation before deorbiting using internal propulsion systems.

MDA will collaborate with OneWeb to conduct a feasibility study identifying a cost-effective design for an Active Debris Removal (ADR) system using flight-proven, robotic capture technology. This capacity will be an important development for the safety of satellite operations and the reduction of debris orbiting Earth.

Title: Behaviour Analytics from Multi Earth Observation Satellite Data. The project aims to generate higher-level fusion results that characterize the behaviours of objects. This represents information that is of high value to users. These results are derived from object-level data using machine learning technologies and by fully exploiting the available very large and diverse EO data archives. These very large datasets also present special challenges including storage, access and processing speed, data representation, and fusion methodology that is this project will address.

Detection and Monitoring of Linear Hazards to Travel on Lake Ice

A study is proposed to investigate linear lake ice hazards during the spring melt period using multi-sensor remote sensing data and ice validation using C-band data. After melt onset, the hazards to late season lake ice travel form first along pressure ridges, leads, and cracks where the mechanical strength of the ice sheet is compromised. The horizontal ice sheet containing linear deformations will be imaged as a time series in the south basin of Lake Winnipeg using spatially complete microwave, optical, thermal, and elevation data from RADARSAT-2, WorldView-4, and an Unmanned Aerial Vehicle. Image data will be collected at eight-time steps, including one during late winter.

Title: Mapping of agricultural soils by Earth observation big data mining (ScanSol). ScanSol aims to create new opportunities for mining large quantities of images acquired via the different Earth observation satellites to provide the agricultural community with reliable information on the stable (texture) and variable (fertility) characteristics of agricultural soils. The most advanced big data mining techniques will be applied to all the optical and SAR images on the site of interest located in the Montérégie, Quebec's agricultural region.