Pasquale received the B.S. (cum laude) and M.S. (cum laude) degrees in telecommunications engineering from the University of Naples Federico II, Naples, in 2008 and 2010, respectively. In 2012 he joined the Surrey Space Centre, University of Surrey, Guildford, U.K., where he was awarded the PhD in electronic engineering in 2016. Since 2015, he is working as Research Fellow at the Surrey Space Centre on several remote sensing projects.
Pasquale was the recipient of the Student Paper Award published on the IEEE Proceedings of the 2015 Asia-Pacific Conference on Synthetic Aperture Radar (APSAR).
Pasquale is a reviewer for peer reviewed journals of Remote Sensing and Earth Observation (IEEE Transactions on Geoscience and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, MDPI Remote Sensing and MDPI Sensors).
- Microwave remote Sensing
- Synthetic Aperture Radars (SARs)
- Multispectral Imagery
- Statistical model for SAR imagery
- SAR ship-detection algorithm
- Data fusion and classification techniques
The UK and Mexico Cooperation to Address Environmental Protection: The Bacalar Case Study,
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.
Generalized-Likelihood Ratio Test for SAR Imagery, Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (8) pp. 3616-3630 IEEE
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.
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
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.
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.
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.
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.
My list of publications and academic indexes can be found here