A5066398830- Remote-Sensing Image Classification
- Advanced Data Compression Techniques
- Remote Sensing in Agriculture
- Remote Sensing and Land Use
- Image and Signal Denoising Methods
- Advanced Image and Video Retrieval Techniques
- Air Quality Monitoring and Forecasting
- Municipal Solid Waste Management
- Spacecraft Design and Technology
- Satellite Communication Systems
- Advanced Image Fusion Techniques
- Remote Sensing and LiDAR Applications
- Distributed and Parallel Computing Systems
- Infrared Target Detection Methodologies
- Building Energy and Comfort Optimization
- Earthquake Detection and Analysis
- Satellite Image Processing and Photogrammetry
- Medical Image Segmentation Techniques
- Disaster Management and Resilience
University of Rome Tor Vergata
2022-2024
e GEOS (Italy)
2023
Brazilian Agricultural Research Corporation
2022
Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV- and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, difference between maximum minimum values – MMD) were computed from 30 images, 2019, used as input NN classifier. The samples manually selected, including forested...
The increase in remote sensing satellite imagery with high spatial and temporal resolutions has enabled the development of a wide variety applications for Earth observation monitoring. At same time, it requires new techniques that are able to manage amount data stored transmitted ground. Advanced on-board processing answer this problem, offering possibility select only interest specific application or extract information from data. However, computational resources exist limited compared...
The Φsat-2 mission from the European Space Agency (ESA) is part of Φsat lineup aimed to address innovative concepts making use advanced onboard processing including Artificial Intelligence. based on a 6U CubeSat with medium-high resolution VIS/NIR multispectral payload (eight bands plus NIR) combined hardware accelerated unit capable running several AI applications throughout lifetime. As images are acquired, and after application dTDI processing, raw data transferred through SpaceWire...
The growing amount of data currently collected by earth observation satellites requires new processing procedures able to manage huge quantity information. Among these, reduction techniques represent a viable solution. In particular, on-board is significant because allows save storage space and bandwidth for transmission the ground. However, algorithm used compression must be preserve key information contained in acquired data, so that applicability still guaranteed different fields work....
Post-disaster analysis poses a significant challenge in Disaster Risk Management during the recovery phase. This study explores advantages of multispectral images from Copernicus Sentinel-2 mission for evaluating long-term urban changes and monitoring post-earthquake reconstruction progress. The innovative methodology focuses on assessing areas buildings, following seismic events. It relies analyzing extended time series spectral reflectance values Red–Green–Blue (RGB) bands, resulting...
The amount of raw data generated by instruments on board Earth Observation (EO) satellites is quite often more than what can be transmitted to the ground, so new advanced on-board processing procedures are required. Artificial Intelligence (AI) and Deep Learning (DL) provide information from EO thanks specific hardware platforms these algorithms used also in space. We present here Convolutional AutoEncoder (CAE)-based algorithm developed for lossy image compression European Space Agency...