Image Processing And Analysis.docx

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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

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