Grants and Contributions

Dalhousie University CubeSat (DUCS) will test a new CubeSat frame made out of lightweight metal which will provide better space navigation capabilities for future CubeSats. It will as well validate the use of onboard solar energy and battery storage technology to power a stabilization wheel that will better control the position of the satellite's instruments.

DUCS will be developed in collaboration with IMP Aerospace and Xeos Technologies, Inc.

Killick-1 will test an existing technology designed to monitor oceans (sea-ice detection and concentration estimation) and fit it on the smaller CubeSat frame, which could lead to cheaper solutions to monitor and collect data on our oceans.

Killick-1 will be developed in collaboration with Newfoundland-based C-Core and the University of Prince Edward Island.

Novel Early Season Crop Type Classifier by Improved Utilization of Multi-frequency PolSAR and Electro-optical Data

A.U.G. Signals Ltd. will utilize multi EO satellite data, including RADARSAT, to perform innovative research and development towards developing a practical early season crop type classification product. Specifically, an early season classification procedure for key summer crops in western Canada such as canola, wheat, soybean and corn will be obtained. This development could also be extended to other crop types and regions. A successful product in this area is essential for the development of Canada-wide crop disease risk monitoring tool and will also help in the early prediction of expected crop production.

A Remote Sensing Virtual Machine incorporating multiple space-based data and M2M connections in a cloud-compatible environment

Kepler has evolved monitoring techniques which use satellite radar data to monitor water extent, water level and water flow, with a focus on wetlands. In this project, improvements will be made via:
- The integration of multiple sources of earth observation data and ancillary data to improve of accuracy of monitoring results;
- State-of-the-art computing technologies preparing Kepler's techniques for expanded use in terms of access by user groups and expanded geographic coverage.

RADARSAT M2M Cloud Platform

The project intends to provide cloud-based processing for RADARSAT-2 and RCM via the SkyWatch platform to enable machine to machine integration. The service will facilitate Earth Observation applications combining RADARSAT data with data from other missions including Sentinel-1 and Sentinel-2 as well as very high resolution commercial products.

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.

ManitobaSAT-1 will study how space conditions affect the composition of asteroids and the Moon so that researchers on Earth can better understand those effects when studying meteorites. This mission will also help better understand the origins of asteroids when the data will be combines with the data from asteroid sample-return missions, such as the OSIRIS-Rex mission.

ManitobaSAT-1 will be developed in collaboration with the University of Winnipeg, York University, Interlake School Division, and Magellan Aerospace.

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.