Elisa Giusti

Dr. Elisa Giusti

CNIT - National Inter-University Consortium for Telecommunications, Pisa, Italy
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Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 February 2013 in IEEE Transactions on Geoscience and Remote Sensing
Verified
Journal Article
Closed access
DODomenico Olivadese
Domenico Olivadese
Elisa GiustiElisa Giusti
Elisa Giusti
DPDario Petri
Dario Petri
MMMarco Martorella
Marco Martorella
Amerigo CapriaAmerigo Capria
Amerigo Capria
FBFabrizio Berizzi
Fabrizio Berizzi
As recently demonstrated, passive radars are able to detect and track targets by exploiting illuminators of opportunity. In this paper, it will be proven that the same concept can be extended to passive inverse synthetic aperture radar (P-ISAR) imaging. A suitable type of signal processing is proposed that is able to form P-ISAR images starting from range-Doppler maps, which represent the output of passive-radar signal processing. Multiple-channel digital television broadcasting (DVB)-T signals are used to demonstrate the concept as they provide enough range resolution to form meaningful ISAR images. The problem of grating lobes, which are generated by the DVB-T signal, is also addressed and solved.
1 June 2012 in IET Radar, Sonar & Navigation
Verified
Journal Article
Closed access
MMM. Martorella
M. Martorella
Elisa GiustiElisa Giusti
Elisa Giusti
FBF. Berizzi
F. Berizzi
ABA. Bacci
A. Bacci
EME. Dalle Mese
E. Dalle Mese
Non-cooperative moving targets typically appear defocused in synthetic aperture radar (SAR) images as they are non-stationary during the coherent processing interval. Typically, the problem of refocusing moving targets in SAR scenes is addressed as a problem of the target's motion estimation and compensation. In this study, the problem is addressed as a problem of non-cooperative target imaging and the authors propose a solution based on the use of inverse synthetic aperture radar (ISAR) processing to solve this problem. Taking into consideration the advantages of SAR processing, the problem is tackled starting from formed SAR images, where non-cooperative targets images are firstly detected, then backprojected to the received data domain and reprocessed as ISAR data. The effectiveness of the proposed method is then tested on Cosmo-Skymed spotlight SAR data of maritime targets.
20 December 2021 in IEEE Aerospace and Electronic Systems Magazine
Verified
Journal Article
Closed access
Amir Hosein OveisAmir Hosein Oveis
Amir Hosein Oveis
Elisa GiustiElisa Giusti
Elisa Giusti
Selenia GhioSelenia Ghio
Selenia Ghio
MMMarco Martorella
Marco Martorella
Abstract—In recent years, convolutional neural networks (CNNs) have drawn considerable attention for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR data analysis that have been tackled by CNNs are systematically reviewed, such as automatic target recognition, land use and land cover classification, segmentation, change detection, object detection and image denoising. Special emphasis has been given to practical techniques such as data augmentation and transfer learning. Complex-valued CNNs, which have been introduced to exploit phase information embedded in SAR complex images, have also been extensively reviewed. To conclude this review paper, open challenges and future research directions are highlighted.
31 January 2011 in IEEE Transactions on Aerospace and Electronic Systems
Verified
Journal Article
Closed access
MMM. Martorella
M. Martorella
Elisa GiustiElisa Giusti
Elisa Giusti
LDLibertario Demi
Libertario Demi
ZZZ. Zhou
Z. Zhou
ACA. Cacciamano
A. Cacciamano
BBB. Bates
B. Bates
Automatic target recognition (ATR) is generally the reason why inverse synthetic aperture radar (ISAR) imaging systems are employed. Moreover, the use of fully polarimetric radar systems in radar imaging applications such as SAR and ISAR has enhanced both image quality and classification capabilities. The authors propose a novel technique for ATR using polarimetric ISAR (Pol-ISAR) images. The proposed method is based on a model matching approach. Results are obtained that show the effectiveness of such a technique.
24 February 2023 in IEEE Transactions on Computational Imaging
Verified
Journal Article
Open access
Ajeet KumarAjeet Kumar
Ajeet Kumar
Elisa GiustiElisa Giusti
Elisa Giusti
FMFrancesco Mancuso
Francesco Mancuso
Selenia GhioSelenia Ghio
Selenia Ghio
ALAlberto Lupidi
Alberto Lupidi
MMMarco Martorella
Marco Martorella
A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration.
17 June 2022 in IEEE Transactions on Aerospace and Electronic Systems
Verified
Journal Article
Closed access
Amir Hosein OveisAmir Hosein Oveis
Amir Hosein Oveis
Elisa GiustiElisa Giusti
Elisa Giusti
Selenia GhioSelenia Ghio
Selenia Ghio
MMMarco Martorella
Marco Martorella
The closed set assumption in conventional classifiers, such as the Softmax, constrains deep networks to select an output from the given known classes. However, classification in a real-world scenario should account for open sets where a new class of targets, which has not been included in the training phase, can easily confuse the classifier. Therefore, it is necessary to not only correctly classify known classes, but also fundamentally deal with unknown ones. In this paper, we extend the Openmax approach, which has been introduced for open set recognition in the optical domain, by offering solutions to its inherent limitations. The motivation behind the work is to propose a more accurate and robust classifier for the open set recognition problem in synthetic aperture radar (SAR) images, without having any prior knowledge about the incoming unknown data. A number of real data experiments are conducted to demonstrate the effectiveness of the proposed method on the basis of selected performance metrics. In particular, the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, which contains SAR images of ten military vehicles, is used for training and inference of a Convolutional Neural Network (CNN), with an option to recognize open set images.