Papers by Pietro Guccione
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Multiple-Input-Multiple Output (MIMO) Synthetic Aperture Radar (SAR) along-track formations can b... more Multiple-Input-Multiple Output (MIMO) Synthetic Aperture Radar (SAR) along-track formations can be used to fraction the power resources into compact, lightweight and cost-effective satellites, or to extend the swath coverage beyond the limit provided by a small antenna. In this second case, the Pulse Repetition Frequency (PRF) is kept low by implementing an inversion that solves up to N-1 ambiguities, given N observations. The simultaneous illuminationthat allows for the N² gain due to the coherent combination of the N transmitters and the N receivers, is analyzed and shown not to be critical, as the more than N=2 sensors are assumed. Performance is evaluated for the N=2 and N=3 cases and compared with the Single Input Multiple Output formations, where one sensor is transmitting, and all are receiving. Finally, the impact of the across-track deviation from the orbit is modeled and evaluated.
2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), 2015
This research examines ground-based radar technology in measuring the displacements and deformati... more This research examines ground-based radar technology in measuring the displacements and deformations of various origin and demonstrates the operational and performance characteristics of ground-based radar through a series of field tests carried out in Atlantic Canada in 2012. The field tests include data collection cases for the ground-based synthetic aperture radar (IBIS-L) and for ground-based real aperture radar (IBIS-S). Some examples of applications include Big Falls HydroElectric Generating Plant, a gravel pit, a building on the Memorial University of Newfoundland (MUN) campus, and vertical and horizontal structures (skywalk bridge and chimney), also located on the MUN campus. The IBIS (Image By Interferometric Survey) ground-based radar sensor was developed by the IDS (Ingegneria dei Sistemi SpA), Italy, and is owned by C-CORE, Canada.
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
In this paper, the full focused processing in the frequency domain for high Pulse Repetition Freq... more In this paper, the full focused processing in the frequency domain for high Pulse Repetition Frequency (PRF) radar altimeter science data is proposed. Considering a radar altimeter as a Synthetic Aperture Radar (SAR) instrument operating in near nadir-looking geometry, a SAR frequency-domain focusing algorithm, namely Range-Doppler algorithm, is analyzed. Then a properly modified processor for radar altimeters is described and implemented. Simulation set-up using parameters from two missions (CryoSat and Sentinel-6) and a preliminary result from in-orbit CryoSat data confirm the effectiveness of the full focused processing in the frequency domain. The proposed approach, compared to the one formerly proposed in literature [1], is less complex and more computationally efficient.
Sensors & Transducers, 2016
Speech Emotion Recognition (SER) is a recent field of research that aims at identifying the emoti... more Speech Emotion Recognition (SER) is a recent field of research that aims at identifying the emotional state of a speaker through a collection of machine learning and pattern recognition techniques. Features based on linear source-filter models have so far characterized emotional content in speech. However, the presence of nonlinear and chaotic phenomena in speech generation have been widely proven in literature. In this work, recurrence properties of vowels are used to describe nonlinear dynamics of speech with different emotional contents. An automatic vowel extraction module has been developed to extract vowel segments from a set of spoken sentences of the publicly available German Berlin Emotional Speech Database (EmoDB). Recurrence Plots (RPs) and Recurrence Quantitative Analysis (RQA) have been used to explore the dynamic behavior of six basic emotions (anger, boredom, fear, happiness, neutral, sadness). Statistical tests have been performed to compare the six groups and check ...
IEEE Transactions on Geoscience and Remote Sensing, 2018
In this paper, the synthetic aperture radar (SAR) calibration for low-frequency missions by means... more In this paper, the synthetic aperture radar (SAR) calibration for low-frequency missions by means of stable point targets is presented. Calibration at low frequency involves the absolute radiometric calibration, the antenna pattern and pointing characterization and validation, and the distortion system parameters' estimation. The use of traditional instrumentation, such as a polarimetric active radar calibrator, a corner reflector, or an active transponder, may be costly and can reduce the time the instrument is used for operational acquisitions. The purpose of this paper is to evaluate the potentiality in calibration of point targets for which the radar cross section and the time stability have been characterized. Given a calibration site, once that a set of the stable point targets have been detected by the analysis of an interferometric stack of SAR acquisitions, they may be used as passive calibrators for the validation of radiometry, elevation antenna pattern, and pointing estimation. We show that, although less targets are expected to be found in P-or L-band than in C-or X-band, a sufficient amount (about 250 targets per acquisition) can provide an accuracy in antenna pattern estimation of about 0.04 dB, if the target accuracy is 0.1 dB at 1σ .
Pattern Recognition, 2017
The automatic classification of hyperspectral data is made complex by several factors, such as th... more The automatic classification of hyperspectral data is made complex by several factors, such as the high cost of true sample labeling coupled with the high number of spectral bands, as well as the spatial correlation of the spectral signature. In this paper, a transductive collective classifier is proposed for dealing with all these factors in hyperspectral image classification. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. The collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. In particular, the innovative contribution of this study includes: (1) the design of an application-specific cotraining schema to use both spectral information and spatial information, iteratively extracted at the object (set of pixels) level via collective inference; (2) the formulation of a spatial-aware example selection schema that accounts for the spatial correlation of predicted labels to augment training sets during iterative learning and (3) the investigation of a diversity class criterion that allows us to speed-up co-training classification. Experimental results validate the accuracy and efficiency of the proposed spectral-spatial, collective, cotraining strategy.
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
In this work we study the problem of focusing Synthetic Aperture Radar (SAR) images by means of C... more In this work we study the problem of focusing Synthetic Aperture Radar (SAR) images by means of Compressive Sensing (CS). The presented technique is introduced as an alternative to the traditional focusing methods, suggesting new modes for data acquisition in a more efficient configurations or on-board processing in case of spaceborne sensors. In this paper the method is tested on both simulated and real images acquired by a Ground Based Synthetic Aperture Radar (GB-SAR), for which images of reduced size can be generated with no difficulty. Results of comparison of CS processing with an exact focusing algorithm are shown in terms of root mean square error of amplitude and phase as a function of the number of focused targets and undersampling of the acquisition lines. Coherence of a CS-processed couple of images is also evaluated. The purpose of the paper is to show the potential of CS applied to SAR systems, regardless (at the moment) of the efficiency in computational load. In particular, we show that the image can be reconstructed without loss of resolution after dropping a fair percentage of the received pulses.
IEEE Geoscience and Remote Sensing Letters, 2021
Data mining applications are becoming a more common tool in understanding and solving educational... more Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this paper, four different classification techniques-decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis-are used to build classifier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics. Although all the classifier models show comparably high classification performances, C5.0 classifier is the best with respect to accuracy, precision, and specificity. In addition, an analysis of the variable importance for each classifier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The findings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve the measurement instruments. INDEX TERMS Artificial neural networks, classification algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines.
Remote Sensing
This paper presents the design and processing of the SAR acquisition technique named frequency sc... more This paper presents the design and processing of the SAR acquisition technique named frequency scanning (f-SCAN), aimed to obtain high sensitivity to targets with low backscattering and to improve the signal-to-noise ratio (SNR) in wide-swath systems. The f-SCAN is an interesting alternative to the scanning on receive method (SCORE), which needs multiple phase centres achieved using the digital beam forming (DBF) technique. f-SCAN requires less hardware complexity than SCORE; at the same time, it improves the sidelobes and ambiguities’ suppression. The elements used in f-SCAN to generate the pencil beam are the true time delay lines (TTDLs) and the phase shifters (PSs). The general methodology to design an f-SCAN spaceborne SAR high-resolution wide-swath (HRWS) system is introduced; emphasis is put on the mathematical definition of the timing parameters and on a novel method of using TTDLs to achieve the full spanning of wide swaths. The processing of f-SCAN data is also considered:...
an inaugural event. Signal, video and image processing constitutes the basis of communications sy... more an inaugural event. Signal, video and image processing constitutes the basis of communications systems. With the proliferation of portable/implantable devices, embedded signal processing became widely used, despite that most of the common users are not aware of this issue. New signal, image and video processing algorithms and methods, in the context of a growing-wide range of domains (communications, medicine, finance, education, etc.) have been proposed, developed and deployed. Moreover, since the implementation platforms experience an exponential growth in terms of their performance, many signal processing techniques are reconsidered and adapted in the framework of new applications. Having these motivations in mind, the goal of this conference was to bring together researchers and industry and form a forum for fruitful discussions, networking, and ideas. We take here the opportunity to warmly thank all the members of the SIGNAL 2016 Technical Program Committee, as well as the nume...
In recent years, hyperspectral sensors for remote sensing of the Earth have become very popular. ... more In recent years, hyperspectral sensors for remote sensing of the Earth have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data must be reduced on-board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature have focused the attention to efficient way of on-board data compression, since this is a challenge task due to the difficult environment (outer space), and due to the limited power and computing resources. The current work proposes a framework for on-board operations such as: automatic recognition of target types or detection of events in near real time, in regions of interest with an unsupervised classifier; the compression of specific regions with different bit rates compared to the remaining acquisitio...
Machine Learning, 2016
Remotely sensed hyperspectral image classification is a very challenging task due to the spatial ... more Remotely sensed hyperspectral image classification is a very challenging task due to the spatial correlation of the spectral signature and the high cost of true sample labeling. In light of this, the collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. In this paper, both these paradigms contribute to the definition of a spectral-relational classification methodology for imagery data. We propose a novel algorithm to assign a class to each pixel of a sparsely labeled hyperspectral image. It integrates the spectral information and the spatial correlation through an ensemble system. For every pixel of a hyperspectral image, spatial neighborhoods are constructed and used to build application-specific relational features. Classification is performed with an ensemble comprising a classifier learned by considering the available spectral information (associated with the pixel) and the classifiers learned by considering the extracted spatio-relational information (associated with the spatial neighborhoods). The more reliable labels predicted by
Emotional content in speech has been so far characterized by features based on linear source-filt... more Emotional content in speech has been so far characterized by features based on linear source-filter models. However, the presence of nonlinear and chaotic phenomena in speech generation have been widely proven in literature. In this work, a novel framework has been developed to explore recurrence properties of vowels and describe nonlinear dynamics of speech. Experiments using a database of short spoken sentences emitted in the six basic emotions (anger, boredom, fear, happiness, neutral, sadness) show preliminary results of the approach.
IEEE Transactions on Geoscience and Remote Sensing, 2022
We discuss a coherent synthetic aperture radar (SAR) formation where N identical sensors transmit... more We discuss a coherent synthetic aperture radar (SAR) formation where N identical sensors transmit at the same time, code, and frequency. This is a particular multiple-input-multiple-output (MIMO) configuration, where the transmitted waveforms interfere together, resulting in an illumination pattern that randomly changes in space and time. Similar to the single-input-multiple-output (SIMO) formations, the diversity provided by the N receiver phase centers can be used to mitigate this interference and reduce the pulse repetition frequency (PRF) for achieving large swath coverage. The good point, in the MIMO case, is that the signal-to-noise ratio (SNR) gain of the system increases, theoretically, with the square of the number of elements. However, residual spurious sidelobes may appear as ghosts of the multiple illuminators. In practice, the power gain is to be optimized, together with ambiguity rejection, sidelobes, and azimuth resolution. The actual performances achievable by these formations in terms of impulse response function (IRF), SNR, and sensitivity to the precise positioning of the sensors are discussed theoretically and based on simulations.
2020 IEEE Radar Conference (RadarConf20), 2020
The paper proposes a close formation of Synthetic Aperture Radar (SAR) satellites that are simult... more The paper proposes a close formation of Synthetic Aperture Radar (SAR) satellites that are simultaneously transmitting and receiving, in a Multiple Input Multiple Output (MIMO) configurations. The received signals can be jointly processed to form a single SAR image with a power gain proportional to the squared number of sensors, or to upsample a low PRF, enabling wide coverage, or finally to retrieve a 3D complex reflectivity, in vertical layers, by a tomographic MIMO configuration. Evaluation of performance and limitations has been carried out in the three cases by running full 2D simulations and focusing.
Radiometric calibration is a fundamental item in the processing of Synthetic Aperture Radar (SAR)... more Radiometric calibration is a fundamental item in the processing of Synthetic Aperture Radar (SAR) data and their exploitation since it allows accurate measures of radar reflectivity. The determination of an accurate azimuth antenna pattern (AAP) and pointing acquires particular relevance for a calibration process. AAP estimation has been traditionally performed using transponders that are high precision and geolocated devices able to provide, when illuminated by a SAR antenna, a fixed RCS with high accuracy. The use of transponders is however difficult and expensive since they require maintenance and accurate and complex calibration. The previous objections justify the search of methods for estimating the AAP using natural targets. The natural targets the research wants to exploit are: sparse nearly over all the acquired images, dense, i.e. many targets can be present in a single image, making the estimation robust; stable in time. In this paper a maximum likelihood estimate of the ...
Despite the growing ubiquity of sensor deployments and the advances in sensor data analysis techn... more Despite the growing ubiquity of sensor deployments and the advances in sensor data analysis technology, relatively little attention has been paid to the spatial non-stationarity of sensed data which is an intrinsic property of the geographically distributed data. In this paper we deal with non-stationarity of geographically distributed data for the task of regression. At this purpose, we extend the Geographically Weighted Regression (GWR) method which permits the exploration of the geographical differences in the linear effect of one or more predictor variables upon a response variable. The parameters of this linear regression model are locally determined for every point of the space by processing a sample of weighted neighboring observations. Although the use of locally linear regression has proved appealing in the area of sensor data analysis, it also poses some problems. The parameters of the surface are locally estimated for every space point, but the form of the GWR regression ...
In this work we present some applications of Ground-Based Synthetic Aperture Radar (GBSAR) interf... more In this work we present some applications of Ground-Based Synthetic Aperture Radar (GBSAR) interferometry to the monitoring of dams, bridges and landslides. A SAR system is a coherent active microwave device able to provide 2D refractivity images of a given area with high spatial resolution, independently of weather conditions and day-night cycle. SAR interferometry is the most notable application of SAR technology. This technique relies on the processing of a time series of coherent SAR images. It gives a powerful tool to produce maps of deformations occurring in infrastructures (dams, buildings, bridges) or terrain. In the last few years the research activity of several remote sensing groups has dealt with the development of GBSAR systems. GBSAR sensors represent a cost-effective solution for the continuous monitoring of small scale deformation phenomena if compared to space and air-borne systems. These systems basically consist of a CW radar mounted on a sliding support and synth...
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Papers by Pietro Guccione