Expand knowledge about Machine Learning techniques applied to Earth Observation (EO) big data.
Using ESA Sentinel-1 data, we envisage the creation of innovative solutions, spurring new services to end-users while contributing to the EO market growth
The MATTCH project aims to apply Machine Learning (ML) techniques to advanced InSAR (Interferometric Synthetic Aperture Radar) analyses over large areas, with the goal of identifying among the huge number of InSAR measurements the ones exhibiting a change in their motion trend or, more generally, an “anomalous behaviour”. This “data screening and data mining” step is extremely important to support the End Users Community in the exploitation of frequently updated (every few days) and highly populated (millions of measurement points) information layers.
EO DATA ACCESS
Free access to Sentinel-1 images. Every 6 days, acquired worldwide.
SqueeSAR® processing chain on a cloud computing base.
Deep Learning architectures applied to the processing chain.
“Hotspots” showing a change in the dynamic of motion.
Driven by the link between TRE Altamira and the Final Users community, Polimi-DEIB has developed a Machine Learning approach, which starts from a Recurrent Neural Network architecture to build Long Short-Term Memory integrated layers.
The prototype has been engineered and ingested into TRE-A’s processing environment on a cloud-based architecture, demonstrating to be comparable in terms of results - detection of measurement points exhibiting changes in trend - and significantly better - in terms of computational cost - with respect to other statistics-based approaches in use.
As a final step, the ML method has been applied to real data. This is the case of the Tuscany Region monitoring project using Sentinel-1 acquisitions, in continuous update. The “new” results were delivered to the Final User (University of Florence, Earth Science Department) and their feedback allowed a fine tuning of the algorithm parameters.
TRE ALTAMIRA is the largest InSAR group worldwide. With over 19 years’ experience, we are globally recognised as leader in measuring ground motion from space with millimetre precision. We provide displacement measurements and mapping solutions from satellite radar (SAR) data that are used in a variety of sectors, including oil & gas, mining, civil engineering and geohazards. Founded in 2000 (TRE) and 1999 (ALTAMIRA), we bring together a multidisciplinary team with excellent knowledge of SAR and InSAR applications. Our core competence is radar imagery processing and we are the reference InSAR provider for NASA (National Aeronautics and Space Administration), CSA (Canadian Space Agency), ESA (European Space Agency), JAXA (Japan Aerospace Exploration Agency), CNES (French Space Agency), DLR (German Centre for Aeronautics and Space) and the World Bank.
Founded in 1863, Politecnico di Milano (POLIMI) is the largest school of architecture, design and engineering in Italy. Its Dipartimento di Elettronica, Informazione e Bioingengeria (DEIB) is one of the largest European ICT departments. Research is the main focus of DEIB, pursued according to the highest international quality standards. The six department sections cluster consolidated competences in systems and control, computer science and engineering, electronics, telecommunications, bioengineering and electrical engineering. They have a broad network of partnerships with the best international institutions, which makes the Department one of the fundamental players in the worldwide scenario of scientific and technological innovation. DEIB is participating in the project with researchers and professors from the Artificial Intelligence and Robotics Laboratory (AIRLab).
Recurrent Neural Networks for Trend Change Detection in InSAR Time SeriesPDF
Presented during the Earth Observation Phi-Week, 9-13/09/2019, ESA-ESRIN Frascati (Rome), and ASAR 2019 organized by CSA, 1-3/10/2019, Saint-Hubert, Quebec (Canada)
Authors: Francesco Lattari (1), Matteo Matteucci (1), Alessio Rucci (2), Christine Bischoff (2), Marco Basilico (2), Emanuele Passera, Marco Bianchi (2) │ (1) Politecnico di Milano, Milano, Italy, (2) TRE ALTAMIRA s.r.l., Milano, Italy