The Italian Projects SardOs and 3xA for a Sustainable and Innovative Environmental Monitoring

The need to protect, monitor and evaluate the impact of natural phenomena on the environment, in recent years, plays a role of considerable importance for society and our planet. In the last decades, Italian Space Agency (ASI) invested significant effort in Earth Observation (EO) using COSMO-SkyMed...

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Bibliographic Details
Main Authors: Boella, Michele, Battagliere, M. L., Falcioni, Gianluca, Pennino, Ilaria, Delogu, Francesco, Manunza, Lorenzo
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Companies
COSMO-SkyMed
Earth Observation
Environmental monitoring
Small satellites
Spaceborne radar
Technological innovation
Urban areas
Online Access:Request full text
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Summary:The need to protect, monitor and evaluate the impact of natural phenomena on the environment, in recent years, plays a role of considerable importance for society and our planet. In the last decades, Italian Space Agency (ASI) invested significant effort in Earth Observation (EO) using COSMO-SkyMed radar satellites (Constellation of Small satellites for Mediterranean basin Observation). Since the beginning of the operational life of this mission, ASI promoted the issue of specific initiatives, based on the exploitation of COSMO-SkyMed data. Innovation and research have allowed a profound change in the data acquisition and acquisition methodologies coming to develop increasingly complex and innovative technologies. The focus of this work is to provide an overview of the innovative products and services proposed and marketed by NeMeA Sistemi srl. NeMeA Sistemi developed new and innovative technologies using integrated data for the monitoring and protection of environmental and anthropogenic health, in coastal and nearby areas. The 3xA project is aimed at spotting environmental changes through time in selected geographical regions. Taking as input two SAR acquisitions at time t 1 and t 2 , the acquisitions are firstly pre-processed, homogenised and finally undergo a completely self-supervised algorithm which takes advantage of DNNs to classify changed/unchanged areas. Results are later validated against real environmental changes by means of the SardOS's output data. This method shows promising results in automatically producing a change map from two input SAR images.
ISSN:2153-7003
DOI:10.1109/IGARSS46834.2022.9884612