A comparative study of operational vessel detectors for maritime surveillance using satellite-borne synthetic aperture radar

This paper presents a comparative study among four operational detectors that work by automatically post-processing synthetic aperture radar (SAR) images acquired from the satellite platforms RADARSAT-2 and COSMO-SkyMed. Challenging maritime scenarios have been chosen to assess the detectors' performance against features such as ambiguities, significant sea clutter, or irregular shorelines. The SAR images which form the test data are complemented with ground truth to define the reference detection configuration, which permits quantifying the probability of detection, the false alarm rate, and the accuracy of estimating ship dimensions. Although the results show that all the detectors perform well, there is no perfect detector, and a better detection system could be developed that combines the best elements from each of the single detectors. In addition to the comparison exercise, the study has facilitated the improvement of the detectors by highlighting weaknesses and providing means for fixing them.Peer ReviewedPostprint (published version

On Data Mining in Inverse Scattering Problems: Neural Networks Applied to GPR Data Analysis

Abstract—This paper presents a (semi-)automatic processing technique for GPR data analysis. Exploiting the generalization capabilities of artificial neural networks (ANN), it will be shown that it is possible to feed a Multi-Layer Perceptron (MLP) with a suitable set of input features in order to determine the permittivity of a ground layer. A detailed performance assessment have proven that the algorithm provides very promising results, reconstructing with high accuracy the dielectric properties of both planar and rough surfaces. Some critical issues have anyway emerged that limit the effectiveness of the method to lossless media. Index Terms—Inverse scattering, artificial neural networks, GPR. Figure 1. GPR system: a transmitting antenna (TX) emits e.m. pulses towards the ground, while a receiving antenna (RX) records the travel time and amplitude of the backscattered wavelets I

An Operational Tool for the Automatic Detection and Removal of Border Noise in Sentinel-1 GRD Products

The presence of border noise in Sentinel-1 Ground Range Detected (GRD) products is an undesired processing artifact that limits their full exploitation in a number of applications. All of the Sentinel-1 GRD products generated before March 2018—more than 800,000—are affected by this particular type of noise. In March 2018, an official fix was deployed that solved the problem for a large portion of the newly generated products, but it did not cover the entire range of products, hence the need for an operational tool that is able to effectively and consistently remove border noise in an automated way. Currently, a few solutions have been proposed that try to address the problem, but all of them have limitations. The scope of this paper is therefore to present a new method based on mathematical morphology for the automatic detection and masking of border noise in Sentinel-1 GRD products that is able to overcome the existing limitations. To evaluate the performance of the method, a detailed numerical assessment was carried out, using, as a benchmark, the ‘Remove GRD Border Noise’ module integrated in ESA’s Sentinel Application Platform. The results showed that the proposed method is capable of very accurately removing the undesired noisy pixels from GRD images, regardless of their acquisition mode, polarization, or resolution and can cope with challenging features within the image scenes that typically affect other approaches

A layer stripping approach for em reconstruction of stratified media

EM technique to reconstruct physical and geometric properties of buried stratified media

Automated Detection of Changes in Built-Up Areas for Map Updating: A Case Study in Northern Italy

Keeping track of changes in urban areas on a large scale may be challenging due to fragmentation of information. Even more so when changes are unrecorded and sparse across a region, like in the case of long-disused production sites that may be engulfed in vegetation or partly collapse when no-one is witnessing. In Belgium the Walloon Region is leveraging Earth observation satellites to constantly monitor more than 2200 redevelopment sites. Changes are automatically detected by jointly analysing time series of Sentinel-1 and Sentinel-2 acquisitions with a technique developed on Copernicus data, based on ad-hoc filtering of temporal series of both multi-spectral and radar data. Despite different sampling times, availability (due to cloud cover, for multispectral data) and data parameters (incidence angle, for radar data), the algorithm performs well in detecting changes. In this work, we assess how such technique, developed on a Belgian context, with its own construction practices, urban patterns, and atmospheric characteristics, is effectively reusable in a different context, in Northern Italy, where we studied the case of Pavia