Speaker: Dr Mang Chen

Date and time: Wednesday 17th February, 12 pm
Venue: Zoom (https://universityofmalta.zoom.us/j/91982840274?pwd=ZGlQeU5NK3lqYUVWU2lRVi9idXNLdz09)

The number of Earth observation satellites has increased drastically over the past decade, where some of these satellites enable the capture of two (or more) images of the same region at quasi real-time. Stereo vision techniques can then be used to automatically compute Digital Elevation Models (DEMs) that are important for a number of domains including hydrology, urban planning and natural hazard detection. These stereoscopically derived DEMs provide an efficient and low-cost means for remote mapping of surface topography over large areas and at multiple times for change detection.

The  Centre National d’Etudes Spatiales (CNES) have developed the Stereo Pipeline for Pushbroom Images (S2P) framework that combines the information obtained from the Satellite together with a stereo matching process to estimate the  DEM. However, the stereo matching process adopted by this framework is based on classical techniques. The aim of the SAtellite TraIning and NETworking (SATINET) project is to adopt deep-learning based techniques to improve the stereo-matching process. WorldView-3 satellite images at a resolution 30cm and airborne Lidar data covering the area of San Fernando in Argentina was adopted in our evaluation. Compared with the inherent shortages of classical techniques in feature extraction for textureless, repeated pattern and occlusion, deep learning methods automatically learn and calculate feature parameters through training. Compared with the 66.85% completeness of the classical techniques (SGBM), our method reaches 74.05%, which is a 7% gain. The results below further show that our approach is more robust when compared to the state-of-the-art method.

The Data Science Research Platform (DSRP) at the University of Malta conducts research in the interdisciplinary field of data science. The scope of the group is to use signal processing, machine learning and statistics to develop innovative techniques and to extract useful knowledge from various data sources in an effective manner to benefit the wider public.

For more information about the DSRP, please visit: https://www.um.edu.mt/platform/dsrp

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