Raffaella Guida

University of Surrey

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Pasquale Iervolino

Airbus (United Kingdom)

Daniele Riccio

University of Naples Federico II

Antonio Iodice

University of Naples Federico II

Giuseppe Ruello

Ingegneria dei Trasporti (Italy)

Maximilian Rodger

University of Surrey

Philip Whittaker

Surrey Satellite Technology (United Kingdom)

Donato Amitrano

Italian Aerospace Research Centre

Salman Khan

Commonwealth Scientific and Industrial Research Organisation

José Fonseca

Universidade Nova de Lisboa

João M. N. Silva

University of Lisbon

Cooperative Institutions

University of Naples Federico II

University of Surrey

Federico II University Hospital

Surrey Satellite Technology (United Kingdom)

Ingegneria dei Trasporti (Italy)

University of Salerno

University of Bath

University of Lisbon

Information Technology University

Sapienza University of Rome

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A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery

Remote Sensing (2022)

Elena Grosso Raffaella Guida

In the field of maritime surveillance with satellite imagery, ship detection with Synthetic Aperture Radar (SAR) is largely successful with the exception of those cases where the target Radar Cross Section (RCS) results very low and hard to detect. The paper presents an automatic algorithm that tackles the problem of ship detection in situations where the ship’s RCS is not measurable but its wake is. At this purpose, the algorithm proposed uses a combination of image processing techniques in order to identify ships exclusively by detecting the wakes they leave behind. Once a ship is identified through its wake, its position in the image, its heading and its speed are determined. The algorithm was developed, tested and validated on SAR imagery from the European Space Agency mission Sentinel-1; the results are here presented and discussed. The algorithm was validated on a total of 43 images and resulted in a ship detection rate of 93% with wakes’ components recognized in more than 80% of the cases analysed.

Position (finance)

10.3390/rs14246223

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Citations (3)

Multispectral vs Synthetic Aperture Radar Data for Canopy Height Estimation

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2023)

João E. Pereira-Pires João M. N. Silva José Fonseca Raffaella Guida André Mora

Canopy Height (CH) is an important variable in any forest inventory, not only by its own information, but also as a proxy variable to estimate other parameters as the above-ground biomass. The CH information can also be helpful to understand the climate change trends, for forest management, and in decision support systems related to wildfires. The growing availability of Remote Sensing observations acquired from different sensors, create an alternative for the CH mapping to field campaigns and Airborne Laser Scanning (ALS) missions. Here a comparison between using Multispectral and Synthetic Aperture Radar sensors for CH estimation is presented. Both used the same Regression Methodology, being achieved a R ² /RMSE between 43.71%-72.85%/0.85-4.03m for Multispectral and 42.12%-62.62%/0.96m-4.49m for SAR, for a total of 17 regions of interest. It is concluded that Multispectral data revealed to be more suitable for the CH mapping.

Multispectral pattern recognition

Tree canopy

10.1109/igarss52108.2023.10283118

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ANALISI DI SENSIBILITA’ PER LA STIMA DELL’ALTEZZA DI EDIFICI DA IMMAGINI SAR

G. Franceschetti Raffaella Guida Antonio Iodice Daniele Riccio Giuseppe Ruello

In the last years, new approaches for feature extraction from high resolution SAR images have been presented. One of them is the deterministic approach for building height retrieval from double reflection contribution on SAR images of urban areas. Actually, promising results can be guaranteed only if a high aprioristic knowledge of involved radar and scene parameters is available, but this requirement is not always satisfied. For this reason, here we investigate the influence of an imperfect knowledge of the scene on the final result of building height estimation. In particular, by means of sensitivity analyses and simulation examples, the influence on the height retrieval of an error on the knowledge of the angle between the building wall and the radar flight trajectory is investigated and discussed.

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New Models for the Analysis of High Resolution SAR Images of Urban Areas

Raffaella Guida Antonio Iodice Daniele Riccio

SAR (Synthetic Aperture Radar) images with very high resolution (till 1 meter) are now provided by new powerful sensors but their analysis, interpretation and classification are very hard when urban areas are under study. In this paper the high resolution in SAR images of built-up areas is faced with a model-based approach allowing detection and correct interpretation of 1 meter geometries.

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Validation of S-band data performance for future spaceborne SAR missions

Antonio Natale Raffaella Guida Rachel Bird Philip Whittaker David L. Hall

Most of the SAR missions of the latest years presented sensors working in X-band (like in the TerraSAR-X and Cosmo-SkyMed missions). Today new SAR missions, working at alternative frequencies like S-band, are under planning/design and will be launched in the upcoming years. In addition, full polarimetry is likely to be implemented in some of those missions. This is the case of the UK sensor NovaSAR-S to be launched by 2013. In order to validate the performance of S-band, especially in comparison with the better known and tested X-band, an airborne demonstrator has been set up to contemporaneously acquire fully polarimetric SAR data in both bands. In this paper we present, compare and discuss results deriving from the application of the H/α and the Pauli decompositions to the fully polarimetric datasets in both bands to understand possible advantages of S-band in specific applications.

Launched

C band

Ku band

S band

X band

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The new dual-texture G distribution for single-look PolSAR data

Salman Khan Raffaella Guida

A new form of G distribution, called the dual-texture G distribution, is proposed for Single-Look Complex (SLC) PolSAR data. Unlike the scalar texture productmodel, the dual texture G distribution is derived considering different texture variables for co-pol and cross-pol (x-pol) channels. The co-pol and x-pol texture variables are modelled by the Generalized Inverse Gaussian (GIG) distribution, separately. The result is a more flexible multivariate distribution. Also Mellin Kind Statistics (MKS) are utilized to analyze GIG textures, observe evidence of dual texture and to estimate shape parameters.

Texture (cosmology)

10.1109/igarss.2012.6351257

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Canopy classification with S-band polarimetric SAR data

Raffaella Guida Antonio Natale Rachel Bird Philip Whittaker Martin Cohen

New Synthetic Aperture Radar (SAR) missions in S-band are currently under design but the potential performance of this microwave frequency is still under discussion. This paper presents the outcomes of a study on canopy classification carried out with fully polarimetric S- and X-band datasets contemporaneously acquired by the Astrium airborne SAR demonstrator. Classical polarimetric decompositions have been applied to investigate the S-band capabilities in vegetation monitoring and preliminary results are here presented.

C band

Space-based radar

Ku band

L band

10.1109/igarss.2012.6352103

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A Web Application for the Automatic Mapping of the Flood Extent on Sar Images

2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI) (2018)

Abdelhakim Benoudjit Raffaella Guida

Flood is causing devastating damages every year all over the world. One way to improve the readiness of stakeholders (res-cue authorities, policy makers, and communities) is by providing flood extent maps promptly after the disaster, preferably in an automated way and with a minimum number of satellite imagery to reduce costs. The web application developed in this paper aims to address this problem by mapping the flood extent automatically from SAR images. This web application is portable since it runs on the internet browser, and allows to perform the classification of the flooding in an automated fashion. Another strong point is the rapidity of the processing: the whole processing time was around 3 to 5 minutes for a subset of 20 million pixels. The inundation map returned by our algorithm was validated against vector files mapped by the United Nations Institute for Training and Research (UNITAR) for the same flood event. Regarding the dataset needed in this study, a pair of a preflood SAR image and an optical image of the same area were used to build a training dataset of water and non-water classes. The learning phase is immediately followed by the classification of the post-flood SAR image into a binary flood map. The web application described in this paper was built with open-source Python libraries which are backed by large communities (Django, Scikit-learn among others). The flood map was eventually displayed on OpenStreetMap maps provided by Mapbox.

Python

10.1109/rtsi.2018.8548435

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High Level Semantic Land Cover Classification of Multitemporal Sar Images Using Synergic Pixel-Based and Object-Based Methods

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2019)

Donato Amitrano Raffaella Guida Pasquale Iervolino

Land cover mapping is one of the classic applications of synthetic aperture radar remote sensing. However, despite of the algorithmic progress in classification techniques, the semantic content of available maps does remain unchanged, with only a few macro-classes (like water, forest, urban, and bare soil) being discriminated in the majority of the works from past years. In this paper, a methodology to extract a higher level semantics from synthetic aperture radar images is presented. It is based on coupling pixel-based clustering with object-based image analysis and contextual information. Preliminary results have been produced from multitemporal SAR datasets over a forest area in Colombia. They demonstrate that the synergic exploitation of pixel and object information can provide higher quality land cover results and more information to map users.

Land Cover

10.1109/igarss.2019.8899109

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The new form of G distribution for single-look PolSAR data

Synthetic Aperture Radar, 2012. EUSAR. 9th European Conference on (2012)

Salman Khan Raffaella Guida

When High Resolution (HR) is required, single-look processing of SAR images may be desirable. The multi-look G distribution has been known to fit homogeneous to extremely heterogeneous data accurately. However, it does not reduce to single-look form for PolSAR data. To eliminate this limitation, the new single-look polarimetric G distribution has been proposed, which has single-look K p and G p 0 distributions as its two special cases. The significance of this distribution becomes evident as it fits X- & S-band sub-meter resolution (≈ 1m 2 ) PolSAR data better than, and X-band decameter resolution (≈ 10m 2 ) PolSAR data as good as, the G p 0 & K p distributions.

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