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Stefania Matteoli
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2020 – today
- 2024
- [c35]Salvatore Maresca, Stefania Matteoli:
Enhancement of Prisma Hyperspectral Products for Improved Data Exploitation. IGARSS 2024: 2239-2242 - [c34]Giovanni Corsini, Nicola Acito, Michael Alibani, Marco Diani, Stefania Matteoli, Salvatore Maresca, Marco Morelli, Emilio Simeone, Luigi D'Amato, Maria Libera Battagliere:
Overview of the Main Activities of Hyperhealth Project - Results of Hyperspectral Prisma Data Exploitation. IGARSS 2024: 2255-2258 - 2023
- [j22]Stefania Matteoli
, James Theiler
Bayesian Target Detection Algorithms for Solid Subpixel Targets in Hyperspectral Images. IEEE Trans. Geosci. Remote. Sens. 61: 1-14 (2023) - [c33]Alessia Tricomi, Nicola Acito, Antonello Aiello
Prisma-Based Advanced Prototype Products: An Overview. IGARSS 2023: 1637-1640 - [c32]Giovanni Corsini, Nicola Acito, Michael Alibani, Marco Diani, Stefania Matteoli, Salvatore Maresca
Hyperhealth - Environmental Impact Assessment On Human Health: Advanced Methods For Hyperspectral Prisma Data Exploitation. IGARSS 2023: 1752-1755 - [c31]Stefania Matteoli, James Theiler:
Closed-Form Non-Parametric Admissible Detector for Solid Sub-Pixel Targets. IGARSS 2023: 2274-2277 - 2022
- [j21]Ermes Lo Piccolo
Measurements of Anthocyanin Content of Prunus Leaves Using Proximal Sensing Spectroscopy and Statistical Machine Learning. IEEE Trans. Instrum. Meas. 71: 1-10 (2022) - [c30]Stefania Matteoli, Marco Diani, Giovanni Corsini:
Bayesian Non-Parametric Detector Based on the Replacement Model. IGARSS 2022: 871-874 - 2021
- [j20]Olivier Besson
Adaptive target detection in hyperspectral imaging from two sets of training samples with different means. Signal Process. 181: 107909 (2021) - [j19]François Vincent
Anomaly detection for replacement model in hyperspectral imaging. Signal Process. 185: 108079 (2021) - [c29]James Theiler, Stefania Matteoli, Amanda Ziemann:
Bayesian Detection of Solid Subpixel Targets. IGARSS 2021: 3213-3216 - [c28]Stefania Matteoli, Giovanni Corsini, Marco Diani:
Invariant Submerged Material Recognition with Fluorescence Lidar and Sparsity-Based Approaches. IGARSS 2021: 5327-5330 - 2020
- [j18]Stefania Matteoli
Closed-Form Nonparametric GLRT Detector for Subpixel Targets in Hyperspectral Images. IEEE Trans. Aerosp. Electron. Syst. 56(2): 1568-1581 (2020) - [j17]Stefania Matteoli
ARTEMIdE - An Automated Underwater Material Recognition Method for Fluorescence LIDAR Invariant to Environmental Conditions. IEEE Trans. Geosci. Remote. Sens. 58(3): 1763-1776 (2020) - [c27]Stefania Matteoli, Marco Diani, Giovanni Corsini:
A Fluorescence Lidar Simulator for the Design of Advanced Water Quality Assessment Methodologies. IGARSS 2020: 3743-3746 - [c26]Stefania Matteoli, Marco Diani, Giovanni Corsini:
Improving Physical and Statistical Models for Detecting Difficult Targets with LRT Detectors in Closed-Form. IGARSS 2020: 3959-3962
2010 – 2019
- 2019
- [c25]Stefania Matteoli, Giovanni Corsini, Marco Diani:
Recognizing Submerged Materials with Fluorescence Lidar without Knowledge of Environmental Conditions. IGARSS 2019: 82-85 - [c24]Stefania Matteoli, Marco Diani, Giovanni Corsini:
Nonparametric Target Detection with Target Strength Estimation for Hyperspectral Images. IGARSS 2019: 449-452 - 2018
- [j16]Stefania Matteoli
Automatic Target Recognition Within Anomalous Regions of Interest in Hyperspectral Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 11(4): 1056-1069 (2018) - [c23]Assaf Dvora, Stefania Matteoli, Stanley R. Rotman, Gil Tidhar, Marco Diani, Mayer E. Aladjem
Hyperspectral Target Detection Using Semi- and Non- Parametric Methods. IGARSS 2018: 2857-2860 - [c22]Stefania Matteoli, Marco Diani, Giovanni Corsini:
Hybrid Parametric - Nonparametric Target Detector for Hyperspectral Images. IGARSS 2018: 4241-4244 - [c21]Stefania Matteoli, Giovanni Corsini:
Underwater Material Discriminability with Fluorescence Lidar in Unknown Environmental Conditions. IGARSS 2018: 4599-4602 - 2017
- [j15]Stefania Matteoli, Laura Zotta, Marco Diani, Giovanni Corsini
POSEIDON: An Analytical End-to-End Performance Prediction Model for Submerged Object Detection and Recognition by Lidar Fluorosensors in the Marine Environment. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10(11): 5110-5133 (2017) - [c20]Stefania Matteoli, Marco Diani, Giovanni Corsini
Target detection experiments with a non-parametric detector on a new hyperspectral data set. IGARSS 2017: 1328-1331 - [c19]Stefania Matteoli, Laura Zotta, Marco Diani, Giovanni Corsini
A framework for predicting underwater object recognition performance with fluorescence LIDAR. IGARSS 2017: 2302-2305 - 2016
- [j14]Nicola Acito, Stefania Matteoli, Alessandro Rossi, Marco Diani
Hyperspectral Airborne "Viareggio 2013 Trial" Data Collection for Detection Algorithm Assessment. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 9(6): 2365-2376 (2016) - [j13]Stefania Matteoli, Marco Diani
Automated Classification of Peach Tree Rootstocks by Means of Spectroscopic Measurements and Signal Processing Techniques. IEEE Trans. Instrum. Meas. 65(3): 724-726 (2016) - 2015
- [j12]Stefania Matteoli, Giovanni Corsini
Automated Underwater Object Recognition by Means of Fluorescence LIDAR. IEEE Trans. Geosci. Remote. Sens. 53(1): 375-393 (2015) - [j11]Laura Zotta, Stefania Matteoli, Marco Diani
AFRODiTE: A FluoRescence Lidar Simulator for Underwater Object DeTEction Applications. IEEE Trans. Geosci. Remote. Sens. 53(6): 3022-3041 (2015) - [c18]Nicola Acito, Marco Diani
Multi-temporal approach to atmospheric effects compensation in hyperspectral image classification. IGARSS 2015: 1670-1673 - [c17]Stefano Pignatti
Environmental products overview of the Italian hyperspectral prisma mission: The SAP4PRISMA project. IGARSS 2015: 3997-4000 - [c16]Stefania Matteoli, Laura Zotta, Marco Diani
Fluorescence LIDAR system modeling for underwater object recognition performance evaluation. IGARSS 2015: 4943-4946 - [c15]Nicola Acito, Giovanni Corsini
Validation of forward modeling target detection approach on a new hyperspectral data set featuring an urban scenario and variable illumination conditions. IGARSS 2015: 5075-5078 - 2014
- [j10]Stefania Matteoli, Tiziana Veracini, Marco Diani
Background Density Nonparametric Estimation With Data-Adaptive Bandwidths for the Detection of Anomalies in Multi-Hyperspectral Imagery. IEEE Geosci. Remote. Sens. Lett. 11(1): 163-167 (2014) - [j9]Stefania Matteoli, Tiziana Veracini, Marco Diani
A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors. IEEE Geosci. Remote. Sens. Lett. 11(1): 323-327 (2014) - [j8]Stefania Matteoli, Marco Diani
An Overview of Background Modeling for Detection of Targets and Anomalies in Hyperspectral Remotely Sensed Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 7(6): 2317-2336 (2014) - [j7]Stefania Matteoli, Marco Diani
Impact of Signal Contamination on the Adaptive Detection Performance of Local Hyperspectral Anomalies. IEEE Trans. Geosci. Remote. Sens. 52(4): 1948-1968 (2014) - 2013
- [j6]Nicola Acito, Stefania Matteoli, Marco Diani
Complexity-aware algorithm architecture for real-time enhancement of local anomalies in hyperspectral images. J. Real Time Image Process. 8(1): 53-68 (2013) - [j5]Stefania Matteoli, Tiziana Veracini, Marco Diani
Models and Methods for Automated Background Density Estimation in Hyperspectral Anomaly Detection. IEEE Trans. Geosci. Remote. Sens. 51(5-1): 2837-2852 (2013) - [c14]Stefano Pignatti, Angelo Palombo
The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring. IGARSS 2013: 4558-4561 - 2012
- [c13]Stefano Pignatti
Development of algorithms and products for supporting the Italian hyperspectral PRISMA mission: The SAP4PRISMA project. IGARSS 2012: 127-130 - [c12]Stefania Matteoli, Marco Diani
Effects of signal contamination in RX detection of local hyperspectral anomalies. IGARSS 2012: 4845-4848 - [c11]Nicola Acito, Marco Diani
Effects of the signal dependent noise on the CFARness of the RX algorithm in hyperspectral images. IGARSS 2012: 5474-5477 - 2011
- [j4]Stefania Matteoli, Marco Diani
Hyperspectral Anomaly Detection With Kurtosis-Driven Local Covariance Matrix Corruption Mitigation. IEEE Geosci. Remote. Sens. Lett. 8(3): 532-536 (2011) - [j3]Tiziana Veracini, Stefania Matteoli, Marco Diani
Nonparametric Framework for Detecting Spectral Anomalies in Hyperspectral Images. IEEE Geosci. Remote. Sens. Lett. 8(4): 666-670 (2011) - [j2]Stefania Matteoli, Nicola Acito, Marco Diani
An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection. IEEE Trans. Geosci. Remote. Sens. 49(2): 790-800 (2011) - [j1]Stefania Matteoli, Emmett J. Ientilucci, John P. Kerekes
Operational and Performance Considerations of Radiative-Transfer Modeling in Hyperspectral Target Detection. IEEE Trans. Geosci. Remote. Sens. 49(4): 1343-1355 (2011) - [c10]Stefania Matteoli, Marco Diani
A kurtosis-based test to efficiently detect targets placed in close proximity by means of local covariance-based hyperspectral anomaly detectors. WHISPERS 2011: 1-4 - [c9]Tiziana Veracini, Stefania Matteoli, Marco Diani
An anomaly detection architecture based on a data-adaptive density estimation. WHISPERS 2011: 1-4 - 2010
- [c8]Tiziana Veracini, Stefania Matteoli, Marco Diani
Robust hyperspectral image segmentation based on a non-Gaussian model. CIP 2010: 192-197 - [c7]Stefania Matteoli, Nicola Acito, Marco Diani
A new algorithm for local background suppression in hyperspectral target detection. WHISPERS 2010: 1-4 - [c6]Tiziana Veracini, Stefania Matteoli, Marco Diani
A spectral anomaly detector in hyperspectral images based on a non-Gaussian mixture model. WHISPERS 2010: 1-4
2000 – 2009
- 2009
- [c5]Tiziana Veracini, Stefania Matteoli, Marco Diani
Fully Unsupervised Learning of Gaussian Mixtures for Anomaly Detection in Hyperspectral Imagery. ISDA 2009: 596-601 - [c4]Stefania Matteoli, Nicola Acito, Marco Diani
Local approach to orthogonal subspace-based target detection in hyperspectral images. WHISPERS 2009: 1-4 - [c3]Stefania Matteoli, Emmett J. Ientilucci, John P. Kerekes
Comparison of radiative transfer in physics-based models for an improved understanding of empirical hyperspectral data. WHISPERS 2009: 1-4 - [c2]Stefania Matteoli, Emmett J. Ientilucci, John P. Kerekes
Forward Modeling and Atmospheric Compensation in hyperspectral data: Experimental analysis from a target detection perspective. WHISPERS 2009: 1-4 - 2008
- [c1]Nicola Acito, Giovanni Corsini
A novel technique for hyperspectral signal subspace estimation in target detection applications. IGARSS (3) 2008: 95-98
Coauthor Index
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last updated on 2024-09-28 02:22 CEST by the dblp team
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