Biography

Research

Research interests

My publications

Publications

Iervolino P, Guida R, Whittaker P (2013) NOVASAR-S AND MARITIME SURVEILLANCE, 2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) pp. 1282-1285 IEEE
Pasquale I, Martin C, Raffaella G, Philip W (2014) Ship-detection in SAR imagery using Low Pulse Repetition Frequency Radar, 10TH EUROPEAN CONFERENCE ON SYNTHETIC APERTURE RADAR (EUSAR 2014) VDE VERLAG GMBH
Iervolino P, Diessa V, Iodice A, Ricciardi A, Riccio D, Guida R (2011) A new local approach for flooding level estimation in urban areas using single SAR images, 2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings pp. 257-260
The aim of this paper is to evaluate the level of flooding in proximity of sensible targets in urban areas using only one Synthetic Aperture Radar (SAR) image. To this purpose a two-step algorithm is here proposed: first the flooded areas are detected in the SAR image; and then the water level is retrieved by inverting scattering models developed for urban areas and now properly adapted for the case at issue. The retrieval is performed through a local approach where the a-priori knowledge of the target ground truth and two gauges in the premises is required. The approach is tested on a High Resolution (HR) TerraSAR-X image acquired during the flooding occurred in the Gloucestershire in July 2007. © 2011 IEEE.
Guida R, Ng SW, Iervolino P (2015) S- and X-band SAR Data Fusion, 2015 IEEE 5TH ASIA-PACIFIC CONFERENCE ON SYNTHETIC APERTURE RADAR (APSAR) pp. 578-581 IEEE
Guida R, Iervolino P, Freemantle T, Spittle S, Minchella A, Marti P, Napiorkowska M, Howard G, Arana HH, Alvarado SC (2016) Earth Observation for the Preservation of the Bacalar Area, Living Planet Symposium 2016 Spacebooks Online
Iervolino P, Guida R, Whittaker P (2015) A NEW GLRT-BASED SHIP DETECTION TECHNIQUE IN SAR IMAGES, 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) pp. 3131-3134 IEEE
Iervolino P, Guida R, Whittaker P (2014) ROUGHNESS PARAMETERS ESTIMATION OF SEA SURFACE FROM SAR IMAGES, 2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) pp. 5013-5016 IEEE
Iervolino P, Guida R, Whittaker P (2015) A novel ship-detection technique for Sentinel-1 SAR data, 2015 IEEE 5TH ASIA-PACIFIC CONFERENCE ON SYNTHETIC APERTURE RADAR (APSAR) pp. 797-801 IEEE
Iervolino P, Guida R, Whittaker P (2016) A Model for the Backscattering From a Canonical Ship in SAR Imagery, IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 9 (3) pp. 1163-1175 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Iervolino P, Guida R, Iodice A, Riccio D (2014) Flooding Water Depth Estimation With High-Resolution SAR, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 53 (5) pp. 2295-2307 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Iervolino P, Guida R, Iodice A, Riccio D (2012) FLOODING LEVEL ESTIMATION IN URBAN AREAS WITH SAR IMAGES,
One of the main goals of the State is to guarantee the security and welfare of the citizens. States have agreed in making ?a better world? for citizens under the United Nations (UN) Sustainable Development Goals (SDG) targets and actions. States have acquired the obligation to address this mandate and seek all possible solutions to address it. International cooperation and the use of space technology are tools to achieve this endeavor. This paper discusses the innovations of international cooperation introduced by States and its impact in law-making agreements focusing on Climate Change effects and the protection of the environment under the SDG 13. It explains the innovation in cooperation and law-making procedures by taking as an example the UK-Mexico cooperation for the protection of the Bacalar region and its ecosystem within the International Partnership Space Programme (IPSP). Within this scenario, an Earth observation (EO) product for the flood detection using Synthetic Aperture Radar (SAR) images is presented and efficiently tested over two images acquired during the Hurricane Dean in 2007.
Disclaimer: The information and contents in this paper are solely the original work of the author and does not claim to represent the views of the UKSA, AEM and/or its space partners.
Iervolino P, Guida R (2017) A Novel Ship Detector Based on the
Generalized-Likelihood Ratio Test for SAR Imagery,
Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (8) pp. 3616-3630 IEEE
Ship detection with synthetic aperture radar (SAR)
images, acquired at different working frequencies, is presented
in this paper where a novel technique is proposed based on the
generalized-likelihood ratio test (GLRT). Suitable electromagnetic
models for both the sea clutter and the signal backscattered from
the ship are considered in the new technique in order to improve the
detector performance. The GLRT is compared to the traditional
constant false alarm rate (CFAR) algorithm throughMonte?Carlo
simulations in terms of receiver operating characteristic (ROC)
curves and computational load at different bands (S-, C-, and X-).
Performances are also compared through simulations with different
orbital and scene parameters at fixed values of band and
polarization. The GLRT is then applied to real datasets acquired
from different sensors (TerraSAR-X, Sentinel-1, and Airbus airborne
demonstrator) operating at different bands (S-, C-, and X-).
An analysis of the target-to-clutter ratio (TCR) is then performed
and detection outcomes are comparedwith an automatic identification
system data when available. Simulations show that the GLRT
presents better ROCs than those obtained through the CFAR algorithm.
On the other side, results on real SAR images demonstrate
that the proposed approach greatly improves the TCR (between
22 and 32 dB on average), but its computational time is 1.5 times
slower when compared to the CFAR algorithm.
Marino A, Iervolino P (2017) Ship detection with cosmo-skymed pingpong data using the dual-pol ratio anomaly detector, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2017) - Proceedings pp. 2050-2053 Institute of Electrical and Electronics Engineers (IEEE)
Extensive work has been carried out on detecting ships using
space-borne Synthetic Aperture Radar (SAR) systems. However,
the identification of small vessels is still challenging especially
when the sea conditions are rough. In this work, a
new detector is proposed based on dual-polarized incoherent
SAR images. Small ships have a stronger cross polarization
accompanied by a higher cross- over co-polarization
ratio compared to sea. This is the rational at the base of the
detector.
The new detector is tested with dual-polarization HH/HV
PINGPONG Cosmo-SkyMed images acquired over the North
Sea. The test area is near Rotterdam where a large number of
ships are expected.
Iervolino P, Guida R, Lumsdon P, Janoth J, Clift M, Minchella A (2017) Ship detection in SAR imagery: a comparison study, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2017) IEEE
This paper presents a ship-detection study with Synthetic Aperture Radar (SAR) images acquired at two different frequencies: X- and C-band. The detection procedure relies on a novel algorithm based on the likelihood functions of both canonical ship target and sea clutter. Spaceborne images were acquired over the same area in the Solent Channel in UK at approximately the same time on the 7th June 2016. Here, datasets are compared in terms of probability of detection (PD), probability of false alarm (PFA) and Target-to-Clutter Ratio (TCR). Detection maps are validated with Automatic Identification System (AIS) data when available and preliminary results show a higher TCR for the X-band SAR image.
Yahia O, Guida R, Iervolino P (2018) Sentinel-1 and Landsat-8 feature level fusion for soil moisture content estimation, Proceedings of the 12th European Conference on Synthetic Aperture Radar (EUSAR 2018) EUSAR
A novel methodology is proposed for soil moisture content (SMC) estimation using the feature level fusion of Senti-nel-1 and Landsat-8 satellite datasets. This fusion consists of concatenating Temperature Vegetation Dryness Index (TVDI) to the feature vector (radar and physical features) of the inversion of the Integral Equation Model (IEM) through Artificial Neural Networks (ANN) to reduce vegetation effects on Sentinel-1 estimation. This methodology is applied on Blackwell farms, Guildford, United Kingdom, where ground truth and satellite data were collected dur-ing 2017. The preliminary SMC estimation results show lower RMSE errors (by 0.474%) and less bias than the IEM inversion method.
Achiri L, Guida R, Iervolino P (2018) Collaborative use of SAR and AIS data from NovaSAR-S for Maritime Surveillance, Proceedings of the 12th European Conference on Synthetic Aperture Radar (EUSAR 2018), Aachen, Germany EUSAR
This paper provides a novel approach for the fusion of Synthetic Aperture Radar (SAR) images and Automatic Identification System (AIS) data for the tracking of vessels over sea areas. At this aim, SAR and AIS data are simulated and optimized for the upcoming NovaSAR-S maritime and stripmap modes. These simulated data are used to test the proposed tracking methodology in real time scenario. The results also give practical guidelines on how to task NovaSAR-S to cover uncooperative vessels over the revisit time of the satellite considering the Doppler shift due to the radial velocity of the target.
Iervolino P, Guida R, Ayesh-Meagher A (2018) Land Classification using a novel Multispectral and SAR data Fusion in Doha area, EUSAR 2018 Proceedings VDE VERLAG GMBH
A new algorithm for land classification is presented in this paper and is based on the fusion of Multispectral, Panchromatic and Synthetic Aperture Radar (SAR) images. The novel approach relies on Generalized Intensity-Hue-Saturation (G-HIS) transform and the Wavelet Transform (WT). The fused image is derived by modulating the SAR texture with the high features details of the Panchromatic WT and by injecting this product at the place of G-HIS high feature details. Finally, a classification is performed on the fused product by using a Maximum Likelihood (ML) classifier. The algorithm has been tested on data acquired by Sentinel-1 (SAR) and Landsat-8 (Multispectral and Panchromatic) over the area of Greater Doha in Qatar in 2017. Results show an increment of 3% in the overall accuracy for the fused product compared to the Multispectral dataset.
Yahia Oualid, Guida Raffaella, Iervolino Pasquale (2018) Weights based decision level data fusion of landsat-8 and sentinel-1 for soil moisture content estimation, Proceedings of IGARSS 2018, the 38th annual symposium of the IEEE Geoscience and Remote Sensing Society (GRSS) Institute of Electrical and Electronics Engineers (IEEE)
A novel decision level data fusion algorithm for soil
moisture content estimation is proposed in this paper.
Firstly, individual estimations are determined, respectively,
from the inversion of the Integral Equation Model (IEM) for
Sentinel-1 and from the Temperature Vegetation Dryness
Index (TVDI) for LANDSAT-8. Then, a feature level fusion
of these methods is performed using an Artificial Neural
Network (ANN). Finally, all estimations including the
feature level fusion estimation are fused at the decision level
using a novel weights based estimation. The area of interest
for this study is Blackwell Farms, Guildford, United
Kingdom and datasets were taken on 17/11/2017 for both
Landsat-8 and Sentinel-1. Estimation from the proposed
decision level fusion method produces a Root Mean Square
Error RMSE (1.090%) which is lower than RMSE of the
individual estimations of each sensor as well as that of the
feature level fusion estimation.
Achiri Lotfi, Guida Raffaella, Iervolino Pasquale (2018) SAR and AIS Fusion for Maritime Surveillance, Proceedings of the 4th International Forum on Research and Technologies for Society and Industry Institute of Electrical and Electronics Engineers (IEEE)
This paper presents a novel approach to fuse
Synthetic Aperture Radar (SAR) images and Automatic
Identification System (AIS) data for maritime surveillance. The
procedure consists of four steps. First, ship detection is performed
in the SAR image using a Constant False Alarm Rate (CFAR)
algorithm; then feature extraction (ship position, heading and size)
is performed on ships detected in the SAR image, the third step
consists in identifying the detected ships and extracting the same
features from the AIS data. The final step is to feed the fusion
block with both features vectors extracted separately from the
SAR and AIS. Here the arithmetic mean function is established.
The algorithm is tested using simulated SAR images and AIS data.
Preliminary results of the fusion of SAR and AIS data are
presented and discussed.
The request for maritime security and safety applications has increased in the recent past. In this scenario, Synthetic Aperture Radar (SAR) sensors are one of the most effective means thanks to their capability to get images independently from daylight and weather conditions.
In the SAR ship-detection field, many algorithms have been presented in literature; however none of them has ever considered the aspects behind the interaction of the electromagnetic wave between the target and the surrounding sea.
This thesis explores the electromagnetic interaction arising between the ship and the sea and, firstly, a novel model to evaluate the Radar Cross Section (RCS) backscattered from a canonical ship is derived. RCS is modelled according to Kirchhoff Approximation (KA) within the Geometric Optics (GO) solution. The probability density function relative to the double reflection contribution is derived for all polarizations and the new model is validated on SAR images showing a good match between the theoretical values and those ones measured on real SAR images.

Then, a novel ship detector, based on the Generalized Likelihood Ratio Test (GLRT) where both the sea and the ship electromagnetic models are considered, is proposed. The GLRT is compared to the CFAR algorithm through Monte Carlo simulations in terms of ROCs (Receiver Operating Characteristic curves) and computational load at different bands (S, C and X). Performances are also compared through simulations with different orbital and scene parameters. The GLRT is then applied to datasets acquired from different sensors operating at different bands: the Target to Clutter Ratio (TCR) is computed and detection outcomes are compared with AIS data. Results show that the GLRT presents better ROCs and greatly improves the TCR, but its computational time is slower when compared to the CFAR algorithm.

Finally, a new approach for ship-detection and ambiguities removal in LPRF (Low Pulse Repetition Frequency) SAR imagery is proposed. The method exploits the range migration pattern and is evaluated on a downsampled SAR image. The algorithm is able to reject the SAR azimuth ambiguities and can be adapted for the upcoming Maritime Mode of the future NovaSAR-S sensor.

Additional publications