Pasquale Iervolino

Novasar-S and maritime surveillance

The paper shows a new algorithm for ship-detection from Synthetic Aperture Radar (SAR) images. The algorithm consists of three main stages: pre-processing, detection and discrimination. In the pre-processing a land mask is obtained considering the different statistics between the sea and the lands backscattered field; the detection stage isolates the bright points over the sea background employing a Constant False Alarm (CFAR) method; while the ships are retrieved, in the discrimination step, by evaluating the scattering contributions of the possible targets detected in the previous stage. The algorithm is tested on an airborne S-band SAR image of Portsmouth harbor, similar to those that will become available with the upcoming UK SAR mission NovaSAR-S.

A Model for the Backscattering From a Canonical Ship in SAR Imagery

Synthetic aperture radar (SAR) sensors represent one of the most effective means to support activities in the sector of maritime surveillance. In the field of ship detection, many SAR-based algorithms have been proposed recently, but none of them has ever considered the electromagnetic aspects behind the interactions of SAR signals with the ship and surrounding waters, with the detection step and rate strongly influenced by relative thresholding techniques applied to the SAR amplitude or intensity image. This paper introduces a novel model to evaluate the radar cross section (RCS) backscattered from a canonical ship adapted, to the case at issue, from similar existing models developed for, and applied to, urban areas. The RCS is modeled using the Kirchhoff approximation (KA) within the geometrical optics (GO) solution and, following some assumptions on the scene parameters, derived by empirical observations; its probability density function is derived for all polarizations. An analysis of the sensitiveness of the RCS to the uncertainty on the input scene parameters is then performed. The new model is validated on two different TerraSAR-X images acquired in November 2012 over the Solent area in the U.K.: the RCS relevant to several isolated ships is measured and compared with the expected value deriving from the theoretical model here introduced. Results are widely discussed and ranges of applicability finally suggested.

Flooding Water Depth Estimation With High-Resolution SAR

The retrieval of flooding levels with high-resolution (HR) synthetic aperture radar (SAR) images is presented in this paper. A new framework is proposed. It is based on the inversion of theoretical scattering models initially developed for nonflooded urban areas and here adapted to the flooding case. Starting from the theory, two possible retrieval approaches have been developed and are the main topic of this paper: two possible retrieval approaches have been developed and are the main topic of this paper: the local Single Image Objects Aware (SIObA) and the global Two Image Area Aware (TIArA). These two approaches are conceived to be applicable under different working conditions and consequently holding different properties and reliability. For each of them, a different algorithm is derived and tested, and the retrieval results are validated on a meaningful data set of HR TerraSAR-X images relevant to the Gloucestershire (U.K.) flooding that occurred in year 2007.

A novel ship-detection technique for Sentinel-1 SAR data

The paper shows a novel algorithm for ship-detection in Synthetic Aperture Radar (SAR) imagery. The algorithm is divided in three main steps: land mask rejection, detection and discrimination. In the first step land pixels are rejected by using Shuttle Radar Topography Mission (SRTM) data; in the second stage the potential ships are detected on a method based on the Generalized Likelihood Ratio Test (GLRT) and, finally, some targets are rejected by removing the azimuth ambiguities and by gathering the target pixels in clusters. The algorithm is tested on a Sentinel-1 image acquired over the Portsmouth harbour and compared with the outcomes coming from a Constant False Alarm Algorithm (CFAR).

Roughness parameters estimation of sea surface from SAR images

Some knowledge of sea state and conditions is input in ship detection algorithms based on inversion of scattering models for Synthetic Aperture Radar (SAR) images. This paper shows a novel technique for the estimation of roughness parameters of the sea surface from SAR images. The estimation procedure is based on the minimization of the absolute error between the Radar Cross Section (RCS) of the sea surface measured on the SAR image and the expected RCS computed using the Kirchhoff approach within the Geometrical Optics (GO) solution. The technique is tested on three different TerraSAR-X images acquired in November 2012 over the Portsmouth harbour in the UK.

A new local approach for flooding level estimation in urban areas using single SAR images

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.

A new GLRT-based ship detection technique in SAR images

This paper introduces a novel technique for ship-detection with Synthetic Aperture Radar (SAR) imagery based on the Generalized Likelihood Ratio Test (GLRT). Firstly, a suitable probability density function for a canonical ship is computed from the adoption of scattering models within the Geometrical Optic (GO) solution. Secondly, the GLRT is derived and the detector performance computed through Monte Carlo simulations. Finally, the GLRT technique is compared to the CFAR (Constant False Alarm Rate) algorithm in terms of ROC (Receiver Operating Characteristic) curves and computational load.

Ship detection in SAR imagery: A comparison study

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.

Ship detection with Cosmo-SkyMed PINGPONG data using the dual-pol ratio anomaly detector

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.

S- and X-band SAR data fusion

This paper investigates the benefits deriving from introducing a wavelet-transform-based fusion framework for multi-frequency Synthetic Aperture Radar (SAR) data. A specific application is considered in the assessment of the fused classification map derived and this is the discrimination of different kinds of oil in sea. S-band and X-band datasets, concurrently acquired from the same airborne platform, have here been used. The findings suggest that fusing S-band and X-band SAR data does improve the oil type discrimination between crude oil and diesel oil used in the exercise, although a more quantitative analysis should be conducted in the future to measure the degree of improvement.