Compressive sensing for interferometric inverse synthetic aperture radar applications

The applicability of interferometric inverse synthetic aperture radar (InISAR) techniques to images reconstructed via compressive sensing (CS)-based algorithms is investigated. Specifically, the three-dimensional (3D) reconstruction algorithm is applied after exploiting CS for data compression and image reconstruction. The InISAR signal model is derived and formalised in a CS framework. A comparison between conventional CS reconstruction and global sparsity constrained reconstruction techniques is performed for different compression rates and different signal-to-noise ratio conditions. Performances on the 2D and 3D reconstructions are evaluated. Results obtained on real data acquired during the NATO-SET 196 trial are shown

Multichannel/Multistatic Inverse Synthetic Aperture Radar

Inverse Synthetic Aperture Radar (ISAR) has proven capabilities for non-cooperative target recognition (NCTR) applications. This sensing imaging technique has been able to provide operational information in a robust manner. Multichannel/Multistatic Inverse Synthetic Aperture Radar systems are of emerging interest as they can exploit spatial diversity, enabling improved performance and new applications. Such systems differ from typical monostatic ISAR systems through consisting of multiple spatially diverse transmitter and receiver sites. Due to this spatial diversity, these systems present challenges in managing their operation as well as in usefully combining the multiple sources of information to give an output to the radar operator. The research presented in this work outlines a variety of methods for utilizing ISAR images in the presence of multiple looks. Specific accomplishments include: the development of a method for three-dimensional (3D) reconstruction of moving targets. This method is based on the use of a dual interferometric ISAR system. The interferometric phases measured from two orthogonal baselines are used to jointly estimate the target's effective rotation vector and the heights of the scattering centres with respect to the image plane. Then, this approach is extended for a bistatic case, for a squinted target geometry and in the case of non-orthogonal baselines. Then, the development of image fusion rules to integrate data from spatially distributed independent ISAR systems is studied and carried out. Two different methods are presented: incoherent and coherent fusion. The technique to be chosen depends on the overlapping of the view angles from each sensor of the multistatic radar network. The algorithms are tested on a variety of simulated and real datasets

A fractal-based approach for oil spill detection and recognition

Each year, ships and industries are damaging the delicate coastal ecosystem in many parts of the world, releasing oil or pollutants into rivers and coastal waters. The offshore environments are also polluted by mineral oil, mainly for the following reasons: • tanker incidents at sea, where large amounts of oil are spilled into the sea; • illegal oil discharges from vessels, during their “normal operating procedures” (i.e., oil dumped during cleaning operations); • natural oil seepage. After a tanker accident, the biggest problems encountered are the difficulty in obtaining an overall view of the affected area, getting a clear idea of the extent of the slick, and predicting the direction in which it will move. For natural oil spills and those caused by humans, it is necessary to plan a regular monitoring programme. The aerial surveys over large areas (e.g., the Mediterranean Sea) to check the presence of oil are limited to daylight and good weather conditions. The Mediterranean Sea is characterized by an extensive marine traffic because it provides maritime access to the Middle East (and the Suez Canal), the Black Sea and Southern Europe. Much of this traffic is caused by oil tankers. This high level of traffic creates a high risk of pollution and even ecological disaster, made worse by the fact that the Mediterranean is a closed sea, and that the pollutants cannot be diluted over a larger area by ocean currents. Oil pollution monitoring in the Mediterranean Sea is normally carried out by aircraft or ships. This is expensive, and is constrained by the limited availability of these resources. The satellite imagery can provide a significant contribution in this field, identifying probable spills over very large areas, then guiding aerial surveys for precise observations of specific locations. In order to provide all-weather and global monitoring of such events, spaceborne Synthetic Aperture Radar (SAR) has been recognized as a cornerstone. This instrument offers the most effective means of monitoring oil pollution. Oil slicks appear as dark patches on SAR images because of the damping effect of the oil film to the sea waves. The sea appears less rough in these areas, and the backscattering is reduced. Hence, such an area would appear darker in a radar image. Automatic detection of oil spill in SAR images has been a wide field of research in the last years, much effort being dedicated to the classification of oil spill candidates (i.e., dark patches in the SAR image). This thesis work belongs to the DESPOS project (Exploitation of COSMO-SkyMed system for Detection of Ships resPonsible of Oil Spills) that aims to define a technique for the detection of the responsible ships of oil spills exploiting the short revisit time of the COSMO-SkyMed (CSK) constellation to identify these oil spills and to correlate them to the detected ships at the same instant, after and/or before the passages of the CSK satellites, offering a tool to support control activities for inspection and monitoring aimed to identify illegal actions in which oil tankers are involved. Many ocean surface signatures in SAR imagery are characterized by relatively low normalized radar cross section values. Distinguishing among these signatures objectively can be very difficult, especially with only the single-band and single-polarization SAR imagery. Employing a fractal spectral characterization of the sea surface, in particular by means of the Fractionally Integrated Autoregressive Moving Average (FARIMA) and Fractionally EXPonential (FEXP) models for the Power Spectral Density (PSD) as a wave number function, the proposed technique will be capable of detecting oil spills using conventional and innovative algorithms to recognize and discriminate these one from other phenomena (look-alikes). Only two parameters belonging to the models are necessary for the classification, making the algorithm faster with a very low computational load. The thesis is organized as follows. In the Chapter 1 is examined the sea surface, the rough surface scattering and look-alike phenomena. In the Chapter 2 a short view about the COSMO-SkyMed system is made, in particular are described the instrument functions, the acquisition mode characteristics, the system performances and the images under test. In the Chapter 3 are described the techniques about speckle noise reduction and the segmentation algorithm that is able to detect all the dark patches in the scene under test. How to recognize and discriminate these dark patches is described in the Chapter 4, in particular are introduced the FARIMA and FEXP model and the classification algorithm, including an analysis about the sea surface spectra, since that is the first step for the application of these models. In the Chapter 5 are shown the experimental results of the techniques described applied to the images under test. Eventually are introduced the conclusions and future works

Ship detection from SAR images based on CFAR and wavelet transform

In this paper the authors propose an innovative two-stage technique for ship detection which is applied to sea synthetic aperture radar (SAR) images. This technique is based on the joint use of Wavelet theory, in particular of the two-dimensional Discrete Wavelet Transform (2D-DWT), and the Constant False Alarm Rate (CFAR) processor. Real data acquired from COSMO-SkyMed (CSK) system have been processed to verify the effectiveness of this proposed new technique

3D ISAR/SAR imaging using multichannel real data

Three-dimensional (3D) target reconstruction provides a more specific description of target features, which would be beneficial for the classification process. In this paper a 3D imaging technique of non-cooperative target acquired by airborne radar system is presented. Traditional two-dimensional (2D) single channel inverse SAR (ISAR) imaging locates each target scattering center in range and Doppler. The third point scatterer coordinate that allows to reconstruct the target in a 3D space is estimated using interferometric phases of two non-parallel baselines and Doppler point scatterer location. The accuracy of the 3D reconstruction depends on the length of the baselines and on the focusing achieved by the 2D imaging process for each channel. The relative movement between the target and the airborne system increases the difficulty to form a well-focused 2D image. The radial component of both radar and target motion affects the image focusing. Moreover traditional image formation method as range/Doppler algorithm introduces further blurring due to the wideband situation. To improve the image quality resulting from standard radial motion compensation techniques and range/Doppler image formation, a method for 2D image processing based on range/velocity data formatting is additionally applied

3D InISAR Imaging By Using Multi-temporal Data

InISAR imaging has proven to be an effective tool to produce 3D target reconstruction. This paper presents results of such a technique applied to real data. A multi-temporal approach is considered to further improve the target 3D recon- struction. Such an approach consists of aligning 3D InISAR reconstructions from a single InISAR system relatively to different time intervals

Bistatic three-dimensional interferometric ISAR image reconstruction

A three-dimensional (3D) bistatic inverse synthetic aperture radar (ISAR) imaging method is proposed in this paper. The proposed method makes use of interferometry and technically speaking, produces a 3D target reconstruction by estimating a scattering center position in 3D Cartesian space. The proposed method makes use of a combined ISAR/interferometry technique that also allows the ISAR image plane to orientation to be estimated. Cross- or L-shaped antenna configurations are discussed, and the effects of the baseline length along the horizontal and vertical direction on the scatterer's position estimation are analyzed in detail. Finally, numerical simulations are used to evaluate the proposed method's performance

Incoherent fusion of 3D InISAR images using multi-Temporal and multi-static data

It has recently been demonstrated that Interferometrie ISAR is able to reconstruct 3D point-like target images. However, such 3D reconstructions are generally composed of a small amount of points as the number of scatterers that can be accurately extracted from the received signal and placed in a 3D space is limited by the SNR associated to each of them. This lack of information may cause difficulties for targets identification and classification. To overcome this issue, a 3D reconstruction method based on the use of multi-Temporal and multistatic data that makes use of an incoherent 3D image fusion technique is presented in this paper

Bistatic three-dimensional interferometric ISAR

An algorithm for three-dimensional (3D) target reconstruction is proposed in this study. Such an algorithm is based on the use of 2D inverse synthetic aperture radar (ISAR) imaging and interferometry. The proposed algorithm is able to operate with both bistatic and squinted geometry, thus allowing its applicability to all possible geometrical configurations. A mathematical foundation of the algorithm is provided in this study followed by a validation through the use of both simulated and real data