Raffaella Guida

Height Retrieval of Isolated Buildings From Single High-Resolution SAR Images

Detection of man-made structures in urban areas, in terms of both geometric and electromagnetic features, from a single, possibly high resolution (HR), synthetic aperture radar (SAR) image is a highly interesting open challenge. Within this framework, a possible approach for the extraction of some relevant parameters, describing the shape and materials of a generic building, is proposed here. The approach is based on sound electromagnetic models for the radar returns of each element of the urban scene. A fully analytical representation of electromagnetic returns from the scene constituents to an active microwave sensor is employed. Some possible applications of feature extractions from real SAR images, based on the aforementioned approach, have already been presented in the literature as first examples of potentiality of a model-based approach, but here, the overall theory is analyzed and discussed in depth, to move to general considerations about its soundness and applicability, and the efficiency of further applications may be derived. For the sake of conciseness, although the proposed approach is general and can be applied for the retrieval of different scene parameters (in principle, anyone contributing to the radar return), we focus here on the extraction of the building height, and we assume that the other parameters are either a priori known (e.g., electromagnetic properties of the materials) or have been previously retrieved from the same SAR image (e.g., building length and width). An analysis of the sensitiveness of the height retrieval to both model inaccuracies and errors on the knowledge of the other parameters is performed. Some simulation examples accompany and validate the solution scheme that we propose.

Model-Based Interpretation of High-Resolution SAR Images of Buildings

High-resolution (HR) synthetic aperture radar (SAR) images of urban areas reveal a large variety of details that, although potentially bringing a lot of information, are often very difficult to interpret. Until now, most of the research activity in this field has been devoted to the attempt to retrieve geometric information on buildings in terms of their positions and sizes, by using simplified geometrical models. However, this approach does not allow us to fully exploit the large amount of information present in HR SAR images. In order to improve information retrieval from such images, and, hence, their interpretation, in this paper, we propose to employ a more refined model that accounts for both geometrical (including fine details) and electromagnetic properties of the building. A meaningful case study is presented to show that the main features appearing on the SAR image of a building can be interpreted by using our geometric and electromagnetic model. In addition, a first example of retrieval of the complex dielectric constant of building materials from a SAR image is presented.

Building feature extraction via a deterministic approach: application to real high resolution SAR images

Interpretation of high resolution SAR (synthetic aperture radar) images is still a hard task, especially when man-made objects crowd the scene under detection. This paper contributes to the analysis of this kind of data by adopting an approach, based on a scattering model, for the retrieval of buildings height from real SAR images and presenting first numerical results.

Efficient Simulation of hybrid stripmap/spotlight SAR raw signals from extended scenes

The hybrid stripmap/spotlight mode for a synthetic aperture radar (SAR) system is able to generate microwave images with an azimuth resolution better than the one achieved in the stripmap mode and a ground coverage better than the one of the spotlight mode. In this paper, time- and frequency-domain-based procedures to simulate the raw signal in the hybrid stripmap/spotlight mode are presented and compared. We show that a two-dimensional Fourier domain approach, although highly desirable for its efficiency, is not viable. Accordingly, we propose a one-dimensional (1-D) range Fourier domain approach, followed by 1-D azimuth time-domain integration. This method is much more efficient than the time-domain one, so that extended scenes can be considered. In addition, it involves approximations usually acceptable in actual cases. Effectiveness of the simulation scheme is assessed by using numerical examples.

Assessment of TerraSAR-X Products with a New Feature Extraction Application: Monitoring of Cylindrical Tanks

There is no doubt that retrieving observed scene features is one of the most interesting and challenging activities in all fields of remote sensing: The successful extraction of scene parameters may not only mean the success of the adopted procedure but also the success of a prediction model, an image product, a sensor project, or even an entire mission. This paper is partly concerned with this. The mission, the sensor, and the products at issue are the TerraSAR-X; the feature retrieval approach is the deterministic model-based approach already tested on E-SAR images and now in phase of improvement and testing on high-resolution TerraSAR-X images. Together with assessing the performances of TerraSAR-X products, this paper deals with a new application which, until now, has not received enough attention even if being worth of it: monitoring of big tanks in suburban or urban areas. Detailed discussion concerning the most suitable product for this kind of application is accompanied by retrieval results carried out on recently acquired TerraSAR-X images.

On Single-Look Multivariate

For many applications where High Resolution (HR) Synthetic Aperture Radar (SAR) images are required, like urban structures detection, road map detection, marine structures and ship detection etc., single-look processing of SAR images may be desirable. The G family of distributions have been known to fit homogeneous to extremely heterogeneous Polarimetric SAR (PolSAR) data very well and can be very useful for processing single-look images. The multi-look polarimetric G distribution has a limitation that it does not reduce to single-look form for (multivariate) PolSAR data. This paper presents the new single-look polarimetric G distribution, which reduces to its two well-known special forms, the single-look Kp and Gp0 distributions, when the domain of its parameters are restricted. The significance of this distribution becomes evident as it fits X- & S-band sub-meter resolution (<; 1 m2) PolSAR data (acquired over the same scene at the same time in X- & S-bands) better than the Gp0 & Kp distributions, while it fits the X-band decameter resolution (10 m2) PolSAR data as good as the Gp0 distribution. Numerical Maximum Likelihood Estimation (MLE) method for parameter estimation of multivariate G, Gp0, and Kp distributions is proposed. Simulated PolSAR data has been generated to validate the convergence and accuracy of maximum likelihood parameter estimates to values corresponding to globally maximum likelihood. A new iterative algorithm for accurate estimation of speckle covariance matrix is also proposed.

{\cal G}

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.

Distribution for PolSAR Data

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.

Novasar-S and maritime surveillance

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 Model for the Backscattering From a Canonical Ship in SAR Imagery

New powerful spaceborne sensors for monitoring urban areas have been designed and are ready for launch. More detailed SAR images will be soon available and, consequently, new tools for their interpretation are needed, above all when urban scenes are illuminated. In this paper, the authors propose some tools for the study and the analysis of high resolution SAR images based on a SAR raw signal simulator for urban areas. Comparing simulated SAR images with the real one, interpretation of SAR data is improved and fundamental support of the employed tools is further assessed.