Image Processing and Analysis; Automatic Classification;
Analysis of Multitemporal Images; Regression and Estimation
Applications Forestry Agriculture Damage Assessment Urban area analysis Building Detection Risk assessment Vegetation monitoring Water quality Snow and ice Climatic changes Image Processing and Analysis
Hyerarchical multilevel segmentation
Multiscale/multilevel feature extraction
Texture extraction
Image denoising
Automatic Classification Statistical methods
Machine learning (neural networks, support vector machine, ect)
Kernel methods
Semisupervised classification
Domain adaptation and active learning methods
Advanced multiscale feature extraction for VHR image classification
Feature selection and classification of hyperspectral images
Accuracy assessment in VHR classification maps
Classification of SAR signals
Analysis of Multitemporal Images
Registration of multitemporal images
Modeling and mitigation of registration noise
Analysis and classification of multi- and hyper-temporal data
Change detection in medium and very high resolution SAR images
Change detection in medium and very high resolution Multispectral images
Change detection in medium and very high resolution Hyperspectral images
Regression and Estimation
Model based regression methods
Regression based on machine learning techniques
Biophysical parameters estimation
RADARS Systems
Synthetic Aperture Radar (SAR)
Ground penetrating radar and radar sounder
Applications
Earth observation
Planet observation
Synthetic Aperture Radar (SAR)
Analysis and modelling of HR and VHR SAR signals
Building detection and 3D reconstruction in VHR SAR images
Change detection (2D and 3D)
Automatic classification
Analysis of temporal series of HR and VHR SAR images.
Ground Penetrating Radar and Radar Sounder
Statistical analysis and characterization of radar sounder signals
Pre-processing techniques (clutter estimation and noise reduction, filtering, etc.)
Feature extraction and automatic classification of subsurface layers or patterns
Design of radar sounding instruments for planetary exploration
Radar sounder data of the subsurface of the North Pole of Mars acquired by the SHARAD instrument, and examples of possible elaborations: (top right) automatic detection of basal returns aimed at the estimation of ice thickness and basal topography; (bottom left) automatic detection of surface clutter returns on radargrams through clutter simulation and matching with real data; (bottom right) automatic detection and characterization of subsurface linear features aimed at the mapping of icy layers.
Radar sounder data of the subsurface of Byrd Glacier Antarctica acquired by the MCoRDS instrument, and examples of possible elaborations:
Examples of ice subsurface target classes backscattering and statistical analysis of the measured radar signal
Automatic detection of ice subsurface targets based on segmentation
Automatic classification of the ice subsurface based on feature extraction and machine learning
Pattern Recognition
Biomedical Signals and Image, Neuroscience, Industrial Visual Inspection, Content-Based Image Retrieval
Methods
Supervised, semisupervised and unsupervised classification
Statistical methods for data analysis
Machine learning (neural networks, support vector machine, etc.)
Kernel-based methods and support vector machines
Domain adaptation and active learning algorithms
Multidimensional signal processing
2D and 3D image processing
Data fusion
Applications
Remote sensing
Biomedical Signals and Images
Neuroscience
Industrial Visual Inspection
Others Biomedical Signals and Images Analysis
of retina images for diseases detection and
mapping Analisys
of MRI and fMRI images
Analysis
of TAC images
Analysis
of ECG and ECoG signals
Analysis
of EEG signals
Development
of Brain Computer Interface (BCI)
systems Neuroscience
Analysis of fMRI signals
Analysis of EEG and MEG signals
Fusion between EEG, MEG and fMRI data
Pattern recognition for cognitive analysis
Laboratory of Functional Neuroimaging, CIMeC.
This activity is developed in cooperation with CIMeC – Centro interdipartimentale Mente/Cervello (Center for Mind/Brain Sciences), University of Trento. Industrial Visual Inspection
Fig Quality Assessment
This activity is developed in cooperation with the Vision-Image Processing and Pattern Recognition Laboratory, Süleyman Demirel University, Isparta, Turkey. Content-Based Image Retrieval (CBIR)
Image Feature Extraction for CBIR problems
Fast content-based Image Retrieval
Relevance Feedback Driven by Active Learning
Multisensor System
Data Fusion
Wireless Sensor Networks
Data Fusion
Multi-resolution fusion (pansharpening)
Multi-sensor classification techniques
Integration of remote sensing, ancillary and in situ data
Architectures for multisensor data analysis
Change Detection in Multi-Temporal Multi-Sensor data
Systems for the integration of Remote Sensing and Wireless Sensor Networks
Remote Sensing-based framework for automatic and scalable Wireless Sensor Networks deployment planning in forests
LiDAR
Light Detection and Ranging
Analysis of multireturn LiDAR data
Automatic classification of LiDAR data
Forest segmentation in the 3D LiDAR cloud space
Estimation of tree height, diameter and volume
Forest mapping using airborne LiDAR data
Building edge detection in the 3D LiDAR cloud space
Fusion between terrestrial and airborne LiDAR data
Fusion between airborne LiDAR data and hyperspectral/multispectral images