This PDF file contains the front matter associated with SPIE Proceedings Volume 8179, including the Title Page, Copyright Information, Table of Contents, Introduction, and the Conference Committee listing.

The ESA Sentinels constitute the first series of operational satellites responding to the Earth Observation needs of the EU-ESA Global Monitoring for Environment and Security programme. The GMES space component relies on existing and planned space assets as well as on new complementary developments by ESA. In particular, as part of the GMES space component, ESA is currently undertaking the development of 3 Sentinels mission families. Each Sentinel is based on a constellation of 2 satellites in the same orbital plane. This configuration allows to fulfil the revisit and coverage requirements and to provide a robust and affordable operational service. The launch of the 2nd satellite is scheduled 18 months after the launch of the 1st spacecraft of the constellation. The lifetime of the individual satellite is specified as 7 years, with consumables allowing mission extension up to 12 years. The lifecycle of the space segment is planned to be in the order of 15-20 years. The strategy for Sentinel procurement and replacement over this period is being elaborated, but will likely result in a need for 4-5 satellites of each type if the desired robustness for the service that GMES will provide is to be achieved. This paper will describe the operational and observational capabilities of the Sentinel-1 mission based on the user requirements, including potential emergency requests. An example of a pre-defined mission timeline for each and every cycle will be given.

This work is developed in the framework of the SOFIA project (ESA AO-6280) which aims at estimating important biophysical variables in the Alpine area by using advanced state of the art retrieval methods in combination with new generation satellite polarimetric SAR data. As a first analysis in this direction, in a previous contribution we investigated the effectiveness of fully polarimetric RADARSAT2 C-band SAR data and proposed the use of the Support Vector Regression technique and the integration of additional information on the investigated area obtained from ancillary data. In this paper we move the attention on the exploitation of L-band SAR data. In more detail, our analysis aims at: 1) assessing the effectiveness of the proposed retrieval algorithm with different satellite SAR data, namely the L-band data; 2) comparing the estimates obtained with the use of C- and L-band SAR imagery, in order to understand common patterns and eventually discrepances due to the different penetration capability of the signals; and 3) understanding the feasibility of a synergic use of L and C band SAR data (when both available) for improving the retrieval of soil moisture in Alpine areas. The experimental analysis is carried out with the use of polarimetric RADARSAT2 (C-band) and ALOS PalSAR (L-band) SAR data. The achieved results indicate the potential of the synergic use of C and L band SAR imagery for the retrieval of soil moisture also in the challenging alpine environment. This feature is properly exploited by the proposed retrieval algorithm, thus pointing out its effectiveness in handling data with different spatial and radiometric characteristics.

The main objective of this research is to develop, test and validate a soil moisture (SMC)) algorithm for the GMES Sentinel-1 characteristics, within the framework of an ESA project. The SMC product, to be generated from Sentinel-1 data, requires an algorithm able to process operationally in near-real-time and deliver the product to the GMES services within 3 hours from observations. Two different complementary approaches have been proposed: an Artificial Neural Network (ANN), which represented the best compromise between retrieval accuracy and processing time, thus allowing compliance with the timeliness requirements and a Bayesian Multi-temporal approach, allowing an increase of the retrieval accuracy, especially in case where little ancillary data are available, at the cost of computational efficiency, taking advantage of the frequent revisit time achieved by Sentinel-1. The algorithm was validated in several test areas in Italy, US and Australia, and finally in Spain with a 'blind' validation. The Multi-temporal Bayesian algorithm was validated in Central Italy. The validation results are in all cases very much in line with the requirements. However, the blind validation results were penalized by the availability of only VV polarization SAR images and MODIS lowresolution NDVI, although the RMS is slightly > 4%.

Recently we proposed a Polarimetric Two-Scale Model (PTSM) [1-3], able to retrieve surface roughness, ground permittivity and soil moisture content by processing polarimetric Synthetic Aperture Radar (SAR) data. In our model we consider a bare soil surface as composed of large-scale variations on which a small-scale roughness is superimposed. In particular, the large-scale roughness is locally treated by replacing the surface with a slightly rough tilted facet, whose slope is the same of the smoothed surface at the center of the pertinent facet. The facet slopes along azimuth and range directions are modeled as independent Gaussian variables. Unlike what is described in [1-3], here the facet slope means are not forced to be equal to zero and then our retrieval algorithm can be applied even on not flat areas, just considering information provided by Digital Elevation Models (DEM). The facet's tilt causes the rotation of the local incidence plane around the line of sight and the variation of the local incidence angle around the radar look angle. We accounted for both these effects to evaluate analytically the normalized radar cross sections (NRCS), employed to retrieve the roughness and the soil moisture content using the co-pol/cross-pol method.

The Italian Space Agency is funding 27 scientific projects in the framework of Cosmo/Skymed program (hereafter CSK) . A subset of them are focusing on the improvements of the quality and quantity of information which can be extracted from X-SAR data if integrated with other independent techniques like GPS or SAR imagery in L and C bands. The GPS observations, namely zenith total delays estimated by means of GPS ground stations, could be helpful to estimate the troposphere bias to remove from IN-SAR imagery. Another contribution of GPS could be the improvements of the orbits of Cosmo/SkyMed satellites. In particular the GPS navigation data of the CSK satellites could serve to improve the atmospheric drag models acting on them. The integration of SAR data in L and C bands on the other hand are helpful to investigate land hydrogeology parameters as well as to improve global precipitation observations. The combined use of L, C and X SAR data with different penetration depth could give profiles of land surface properties, especially in forest and snow/ice-packs. For what concern the use of X-SAR imagery for rain precipitation monitoring, particular attention will be paid to its polarimetric properties that we plan to determine aligning the CSK observations with those obtained with ground L and C radars. Anyway the study goals, the approaches proposed, the test sites identified and the external data selected for the development and validation will be described for each project. Particular attention will be paid to single the advantages that the research activities can benefit from the added potentials of CSK system: the more frequent revisiting time and the higher resolution capabilities.

High-resolution and high-frequency COSMO-SkyMed images acquired in the period between 26 April 2010 and 5 April 2011 over the test site in South Tyrol (Northern Italy) offer the chance to analyze the snow changes and to infer information about the physical characteristic of the snow. The X-band sensitivity to snow status was analyzed using two different electromagnetic approaches: 1st Radiative Transfer model, IEM, and a multi-scattering and multi-layer snow scattering model. It results that the description of the dry snow requires a more detailed information about the underlying layers to extract information about the volumetric and ground contribution of the snowpack. The comparison between multi-scattering and multi-layer model predictions and SAR data indicates a better agreement between the measurements and co-polarized backscattering values with respect to the cross polarized backscattering values which appears to be lower than expected indicating that a detailed description on the land surface parameters might help to generate more accurate simulations. The change detection technique for the detection of wet snow was investigated to obtain snow cover map. By using the threshold of -3dB the two frequency distributions for the snow and no-snow areas, are wellseparated only in the case of wet snow areas; on the contrary it results that, at the beginning of the melting season, the frequency distribution still overlaps. From the comparison with LANDSAT 7 ETM+ derived snow map, the omission error of 9.11% and the commission error of 1.84% confirm the typical underestimation of snow cover from SAR images with respect to optical images.

The integrated management of water resources is a crucial problem for improving the quality of life in Sub-Saharian Africa. Several satellites everyday acquire a huge amount of physical information that could be employed as a support for solving agriculture and water problems. In this paper we present a project devoted to exploit the use of high resolution synthetic aperture radar (SAR) images for water resource management at no cost for the users. A case study is developed in the Yatenga region, in the northern Burkina Faso, integrating hydrologic and remote sensing models in order to improve the capacity of predicting flood and drought events. Main attention is posed here on the innovative fractal techniques developed for the extraction of geometrical and physical parameters that can be used for calibrating hydro-geological models.

In contrast to remote sensing with optical sensors, synthetic aperture radar (SAR) satellites require a slant imaging geometry for image acquisition. This fact and because SAR systems operate their sensors actively emphasize that the resulting shadowing effects can have crucial influence on the information content of the image product. Additionally, information retrieval is aggravated by layover effects, where e.g. signatures of target objects superimpose with clutter information. Especially for security applications, the prediction of the expected information content and the calculation of layover and shadow regions during mission planning could greatly improve the image product. This paper presents a toolset to optimize imaging geometry parameters for the image acquisition of a SAR sensor, that performs simulation techniques for finding layover and shadow regions in a given target scene. The described methods will be verified by applying them to TerraSAR-X system parameters and image data.

Maritime surveillance problems are drawing the attention of multiple institutional actors. National and international security agencies are interested in matters like maritime traffic security, maritime pollution control, monitoring migration flows and detection of illegal fishing activities. Satellite imaging is a good way to identify ships but, characterized by large swaths, it is likely that the imaged scenes contain a large number of ships, with the vast majority, hopefully, performing legal activities. Therefore, the imaging system needs a supporting system which identifies legal ships and limits the number of potential alarms to be further monitored by patrol boats or aircrafts. In this framework, spaceborne Synthetic Aperture Radar (SAR) sensors, terrestrial AIS and the ongoing satellite AIS systems can represent a great potential synergy for maritime security. Starting from this idea the paper develops different designs for an AIS constellation able to reduce the time lag between SAR image and AIS data acquisition. An analysis of SAR-based ship detection algorithms is also reported and candidate algorithms identified.

The new Very High Resolution radar satellites, with a spatial resolution up to 1 meter, give a unique opportunity in the context of urban applications. This paper presents an approach for automatic detection of built-up areas based on the analysis of single-polarized TerraSAR-X images. The proposed methodology includes a specific preprocessing of the SAR data and an automated image analysis procedure. The preprocessing aims at providing a multi-resolution texture layer based on the analysis of local speckle characteristics to automatically extract settlements. The technique is tested on 2 TerraSAR-X images acquired over the city of Pavia, northern Italy, in February3 2008. The overall accuracies between 78% and 85% for the derived city footprints demonstrate the high potential of the proposed analysis for built up areas detection. In addition, the joint use of both acquisitions allow to reach a total accuracy of 89%. Although the methodology needs to be further tested on different case studies, the investigation demonstrates the feasibility and the utility of the combined use of ascending and descending SAR intensities data for complete urban footprint extraction.

Goal of this paper is the development and evaluation of a fully automatic method for quality assessment of despeckled synthetic aperture radar (SAR) images. The rationale of the new approach is that any structural perturbation introduced by despeckling, e.g. a local bias of mean or the blur of a sharp edge or the suppression of a point target, may be regarded either as the introduction of a new structure or as the suppression of an existing one. Conversely, plain removal of random noise does not change structures in the image. Structures are identified as clusters in the scatterplot of original to filtered image. Ideal filtering should produce clusters all aligned along the main diagonal. In practice clusters are moved far from the diagonal. Clusters' centers are detected through the mean shift algorithm. A structural change feature is defined at each pixel from the position and population of off-diagonal cluster, according to Shannon's information theoretic concepts. Results on true SAR images (COSMO-SkyMed) will be presented. Bayesian estimators (LMMSE: liner minimum mean squared error: MAP: maximum a-posteriori probability) operating in the undecimated wavelet domain have been coupled with segment-based processing. Quality measurements of despeckled SAR images carried out by means of the proposed method highlight the benefits of segmented MAP filtering.

ScanSAR is an important imaging mode of operation for SAR systems. It allows extended range coverage albeit at the expense of azimuth resolution. Compared to stripmap, ScanSAR is used more for large swath coverage for mapping and monitoring over a wide area. Applications are numerous and include boreal forest mapping, wetland mapping and soil moisture monitoring. The goal of the present work was thus to explore the possibility of processing ScanSAR data optronicaly. Tests were performed with artificially bursted ASAR stripmap data demonstrating that reconstruction of ScanSAR data using the optronic SAR processor is feasible. This paper describes specifically how the data control and handling of ScanSAR data is performed to make it compatible with the optronic processor that was otherwise specifically designed for stripmap processing. As well, the ScanSAR images generated optronicaly are presented.

The management of the monitoring oil spills over the sea surface is a very important and actual task for international environmental agencies, due to the continuous risks represented by possible accidents involving either rigs or tankers. On the other hand the increase of remote sensing space missions can definitely improve our capabilities in this kind of activity. In this paper we consider the dramatic Gulf of Mexico oil spill event of 2010 to investigate on the types of information that could be provided by the available SAR images collection which included different polarizations and bands. With an eye to the implementation of fully automatic processing chains, an assessment of a novel segmentation technique based on PCNN (Pulse Coupled Neural Networks) was also carried out.

Environment Canada's Integrated Satellite Tracking of Pollution (ISTOP) program uses RADARSAT-2 data to vector pollution surveillance assets to areas where oil discharges/spills are suspected in support of enforcement and/or cleanup efforts. RADARSAT-2's new imaging capabilities and ground system promises significant improvement's in ISTOP's ability to detect and report on oil pollution. Of specific interest is the potential of dual polarization ScanSAR data acquired with VV polarization to improve the detection of oil pollution compared to data acquired with HH polarization, and with VH polarization to concurrently detect ship targets. A series of 101 RADARSAT-2 fine quad images were acquired over Coal Oil Point, near Santa Barbara, California where a seep field naturally releases hydrocarbons. The oil and gas releases in this region are visible on the sea surface and have been well documented allowing for the remote sensing of a constant source of oil at a fixed location. Although the make-up of the oil seep field could be different from that of oil spills, it provides a representative target that can be routinely imaged under a variety of wind conditions. Results derived from the fine quad imagery with a lower noise floor were adjusted to mimic the noise floor limitations of ScanSAR. In this study it was found that VV performed better than HH for oil detection, especially at higher incidence angles.

In this paper we present a technique for the analysis of low intensity patches on SAR oceanic amplitude images. The proposed technique, which is based on multifractal analysis of the edges of dark areas (here called regions of interest, ROIs), can be used to identify oil slicks generated by moving ships. The core idea is that different physical-chemical interactions of oil slicks and look-alikes with the sea surface imply different multifractal features for the edges of the ROIs on the acquired images. Accordingly, we propose to perform a multifractal analysis on ROIs' edges, which consists in the estimation of their multifractal spectrum and in the evaluation of the "dispersion area" of this spectrum. The proposed procedure is tested on simulated SAR images and methods and results are extensively discussed. First results seem to indicate that the observation of multifractal spectra is useful in order to distinguish between oil slicks generated by moving ships from other kinds of slicks, even when these phenomena have the same degree of irregularity and an estimation of the classical fractal dimension is not suitable for discrimination purposes.

Synthetic Aperture Radar (SAR) systems represent the most powerful tool to monitor flood events because of their all-weather capability that allows them to collect suitable images even in cloudy conditions. The quality of the flood monitoring using SAR is increasing thanks to the improved spatial resolution of the new generation of instruments and to the short revisit time of the present and future satellite constellations. To fully exploit these technological advances, the methods to interpret images and produce flood maps must be upgraded, so that an accurate interpretation of the multitemporal radar signature, accounting for system parameters (frequency, polarization, incidence angle) and land cover, becomes very important. The images collected by the COSMO-SkyMed constellation of X-band radars represent an example of the aforesaid technological advances. This paper presents a case study regarding a flood occurred in Tuscany (Central Italy) in 2009 monitored using COSMO-SkyMed data. It is shown that the interpretation of the radar data is not straightforward, especially in the presence of vegetation and should rely on the knowledge about the radar scattering mechanisms implemented into electromagnetic models. The paper discusses the multitemporal radar signatures observed during the event and describes the approach we have followed to account for the electromagnetic background into a semi-automatic data processing system.

North Anatolian Fault (NAF) has several records of a huge earthquake occurrence in the last one century, which is well-known as a risky active fault. Some signs indicating a creep displacement could be observed on the Ismetpasa segment. It is reported so far that the San Andreas Fault in California, the Longitudinal Valley fault in Taiwan and the Valley Fault System in Metro Manila also exhibit fault creep. The fault with creep deformation is aseismic and never generates the large-scale earthquakes. But the scale and rate of fault creep are important factors to watch the fault behavior and to understand the cycle of earthquake. The purpose of this study is to investigate the distribution of spatial and temporal change on the ground motion due to fault creep in the surrounding of the Ismetpasa, NAF. DInSAR is capable to catch a subtle land displacement less than a centimeter and observe a wide area at a high spatial resolution. We applied InSAR time series analysis using PALSAR data in order to measure long-term ground deformation from 2007 until 2011. As a result, the land deformation that the northern and southern parts of the fault have slipped to east and west at a rate of 7.5 and 6.5 mm/year in line of sight respectively were obviously detected. In addition, it became clear that the fault creep along the NAF extended 61 km in east to west direction.

Satellite SAR Interferometry (InSAR) has already proved its effectiveness in the analysis of seismic events. In fact, measuring the surface displacement field generated by an earthquake can be useful to define fault parameters regarding the geometry (such as dip and strike angles, width, length), the extension of the rupture and the distribution of slip on the fault plain. However, to retrieve the source parameters from InSAR measurements is rather complex since the inversion problem is ill-posed. In this work we propose an inversion approach for retrieving the fault parameters based on neural networks, trained by simulated data sets generated by means of the Okada forward model. The developed work-flow implements a pre-processing step, aiming to reducing the data dimensionality, in order to improve the performance of the neural network inversion. The methodology has been validated by using experimental data sets obtained using different wavelength and representative of different kind of seismic source mechanisms.

We exploited Differential Synthetic Aperture Radar Interferometry (DInSAR) to investigate the geographical and the temporal pattern of ground deformations in the Ivancich landslide area, Assisi, Italy, in the 18.4-year period April 1992 - September 2010. We used SAR data obtained by the European Remote Sensing (ERS-1/2) satellites in the period April 1992 - July 2007, and SAR data captured by the ASAR sensor on board the Envisat satellite in the period October 2003 - September 2010. We used the Small Baseline Subset (SBAS) technique to process the SAR data, obtaining full resolution measurements for multiple radar targets inside and outside the landslide area, and the history of deformation of the individual targets. The geographical pattern of the ground deformation was found consistent with independent topographic information. The deformation time series of the individual targets were compared to the rainfall history in the area. Results revealed the lack of an immediate effect of rainfall on the ground deformation, and confirmed the existence of a complex temporal interaction between the rainfall and the ground deformation histories in the landslide area. Availability of very long, spatially distributed time series of surface deformation has provided an unprecedented opportunity to investigate the history of the active landslide area.

In recent years many new developments have been made in the field of SAR image analysis. The diversity of available SAR imagery allows a wider range of applications to be covered in the domain of risk management and hazard mapping. The work that we propose is based on the analysis of differences in ground deformation measurements extracted from the processing of data stacks acquired at different frequencies. The aim of the project is the definition of criteria that could assist in the selection of the most appropriate SAR mission according to the type of regions of interest. Key factors are geographic localization and land cover. The study is organized in two main parts. First, the impact of sensitivity to motion, land cover characteristics, spatial resolution and atmospheric artifacts is investigated at different wavelengths. Second, the PS density achieved and the capacity to detect and monitor fast and slow motions over urban and rural areas with different frequencies is analyzed. The presented InSAR analyses have been performed using the Stable Point Network (SPN) PSI software developed by Altamira Information.

At Fraunhofer IOSB the SAR simulator suite CohRaSS (Coherent Raytracing SAR Simulator) dedicated to different, sometimes contradictory purposes is being developed. These include the simulation of very large scenes at high resolution for scene analysis purposes, the simulation of large quantities of training chips for classification and the very fast but less realistic simulation of scenes for use in the training of image analysts. These tasks have very different requirements for the simulation that cannot be met by one single program. Thus different, custom-tailored approaches for each of these tasks are being developed. This paper deals with the main aspects concerning the simulation of training chips for ATR and the simulation of large scenes at very high resolution. Special focus is set on the different approaches used for these tasks from a computational point of view. For both simulators, sample simulated images are shown.

Change detection provides a powerful means for the initial detection of small target objects of interest. However, speckle effects mean this type of approach can be difficult to apply to Synthetic Aperture Radar (SAR) imagery. This paper examines methods for object detection using change between a registered pair of SAR images. The techniques discussed are designed to detect change over small areas ranging in size from a few to perhaps a few hundred pixels. The techniques considered include the ratio of pixels and the ratio of variances covering small regions. The former is a straightforward approach and can provide a good performance baseline. The latter utilises the observation that many man-made objects have a somewhat spiky scattering response, the variance tends to capture this type of response and the ratio of variance enables comparison. Ideally any test statistic should be characterized by a known statistical distribution such that formal tests of a null hypothesis might be carried out. Here the null hypothesis corresponds to no change, and knowledge of the distribution of the test statistic enables the implementation of a Constant False-Alarm Rate (CFAR) detection process. The analysis carried out herein considers the distribution of the ratio statistics under realistic operating parameterisations for target detection in SAR imagery. Results are presented for a registered image pair in the form of detection maps. The simple ratio is found to be considerably more sensitive to image speckle than techniques covering small regions in the imagery.

The response of natural stratification to electromagnetic wave has received much attention in last decades, due to its crucial role played in the remote sensing arena. In this context, when the superficial structure of the Earth, whose formation is inherently layered, is concerned, the most general scheme that can be adopted includes the characterization of layered random media. Moreover, a key issue in remote sensing of Earth and other Planets is to reveal the content under the surface illuminated by the sensors. For such a purpose, a quantitative mathematical analysis of wave propagation in three-dimensional layered rough media is fundamental in understanding intriguing scattering phenomena in such structures, especially in the perspective of remote sensing applications. Recently, a systematic formulation has been introduced to deal with the analysis of a layered structure with an arbitrary number of rough interfaces. Specifically, the results of the Boundary Perturbation Theory (BPT) lead to polarimetric, formally symmetric and physical revealing closed form analytical solutions. The comprehensive scattering model based on the BPT methodologically permits to analyze the bi-static scattering patterns of 3D multilayered rough media. The aim of this paper is to systematically show how polarimetric models obtainable in powerful BPT framework can be successfully applied to several situations of interest, emphasizing its wide relevance in the remote sensing applications scenario. In particular, a proper characterization of the relevant interfacial roughness is adopted resorting to the fractal geometry; numerical examples are then presented with reference to representative of several situations of interest.

In this paper we model those features on a SAR image that are related to "multiple interactions" between different buildings, a phenomenon typical of urban areas characterized by tall and/or closely spaced buildings. We employ a set of conditions, as function of the distance between two subsequent buildings, to verify the occurrence of multiple reflections. We use a deterministic approach to calculate the amplitude value of multiple reflections and determine their position in an azimuth-slant range plane; the used method takes into account the geometric and electromagnetic characteristics of man-made structures. Our method is conceived to model and work with a single high-resolution SAR image of an extended area.

The experimental work on testing the wide-band transmitters and receivers developed for Ka-band and Ku-band radar systems, as well as the signal processing algorithms were introduced. A city light-railway train was selected as the imaged target. The wide-band transmitters and receivers were designed based on the stepped-frequency chirp signal (SFCS) with 2GHz bandwidth synthesized. The Super-SVA technique was used to deal with the case of transmitting SFCS with band gaps between subchirps for purpose of achieving the same bandwidth using as less as possible subpulses. Both Ka-band and Ku-band high-resolution radar images were obtained, which show that Ka-band images are much clear than that of Ku-band as we expect. There are two reasons to explaining this, one reason is due to the electromagnetic scattering of train itself are different for Ka-band and Ku-band frequencies, and the other reason is due to the interactions, i.e. multi-reflection or multi-scattering between the train and the side metal fences or the lamp post are different.

Recent theory of compressed sensing (CS) has been widely used in many application areas. In this paper, we mainly concentrate on the CS in radar and analyze the distinguishing ability of CS radar image based on information theory model. The information content contained in the CS radar echoes is analyzed by simplifying the information transmission channel as a parallel Gaussian channel, and the relationship among the signal-to-noise ratio (SNR) of the echo signal, the number of required samples, the length of the sparse targets and the distinguishing level of the radar image is gotten. Based on this result, we introduced the distinguishing ability of the CS radar image and some of its properties are also gotten. Real IECAS advanced scanning two-dimensional railway observation (ASTRO) data experiment demonstrates our conclusions.

A new despeckling method based on matching pursuit of subband coherent structures of a wavelet-decomposed SAR image is suggested. The iterative pursuit of coherent structures within each subband is organized as an adaptive thresholding of wavelet coefficients using the best wavelet basis chosen from the library of bases minimizing the cost function. The processed image is formed as the cumulative sum of the pseudo images computed by applying of inverse wavelet transform within each iteration. The results of computer modeling have shown the superb quality of the enhanced images obtained by the proposed method in the sense of different criteria as MSE, PSNR, SSIM.

Scattering from a natural surface modeled by a fractional Brownian motion (fBm) two-dimensional process can be evaluated by using the Kirchhoff approximation if proper conditions are satisfied by surface parameters. This evaluation leads to a scattering integral that can be computed via two different asymptotic series expansions, whose behavior has been recently deeply investigated with the aim of finding suitable truncation criteria to compute, with a controlled absolute error, the field scattered by a fractal fBm surface. Based on those results, in this paper truncation criteria are used to compute aforementioned series with a controlled relative error instead of an absolute one. According to such an analysis, an algorithm is provided, which allows to automatically decide which of the two series, if any, can be used, and how it can be properly truncated for efficient and effective computation of the field scattered by natural surfaces. It turns out that by using the standard IEEE double-precision numbering format, a relative accuracy as high as 10^-5 can be achieved for most of allowable values of surface parameters. Finally, to illustrate its practical applicability, the proposed algorithm is employed to generate a Synthetic Aperture Radar (SAR) reflectivity map to be used within a SAR simulation scheme.

Off-shore oil rig is an important facility of oil production in South China Sea. It has a similar back scatter character with ship in SAR image. We present a method of oil platform investigation using multi-temporal SAR remote sensing image in this paper. Firstly, we use ship detection means to find the point target in the SAR imagery. The ship detection means is a CFAR detector. Secondly, we build a model of relevant matching to find the same point target in multi-temporal SAR remote sensing images. If one point target keeps the same position in multi-temporal SAR imagery, we will regard it as an oil platform. Then, we use about some SAR imagery to find the oil platform of South China Sea.

Polarimetric scattering information has a potential application for ship classification and identification in SAR image. This paper investigates in the polarimetric scattering of several types of ships like hospital ship, LPD (Landing Platform Dock), container ship and oil tanker. The scattering characteristics of every ship's pixel is got by using polarimetric decompositions such as Pauli decomposition, SDH (Sphere-Dihedral-Helix) decomposition, Freeman-Durden decomposition, Moriyama decomposition, Yamaguchi decomposition and Cameron decomposition. Then the scattering types of every pixel are fused by voting mechanism. Based on scattering mechanism, the scatterings are merged to four scattering types: sphere scattering, diplane scattering, volume scattering and other scattering. So the polarimetric scattering information of ships has been got. It is shown that hospital ship, LPD, container ship and oil tanker have different polarimetric scattering information. This is useful for ship classification and ship identification.

Near range microwave imaging systems have broad application prospects in the field of concealed weapon detection, biomedical imaging, nondestructive testing, etc. In this paper, the techniques of MIMO and sparse line array are applied to near range microwave imaging, which can greatly reduce the complexity of imaging systems. In detail, the paper establishes two-dimensional near range MIMO imaging geometry and corresponding echo model, where the imaging geometry is formed by arranging sparse antenna array in azimuth direction and transmitting broadband signals in range direction; then, by analyzing the relationship between MIMO and convolution principle, the paper develops a method of arranging sparse line array which can be equivalent to a full array; and the paper deduces the backprojection algorithm applied to near ranging MIMO imaging geometry; finally, the imaging geometry and corresponding imaging algorithm proposed in this paper are investigated and verified by means of theoretical analysis and numerical simulations.

In this paper a fractal based processing for the analysis of SAR images of natural surfaces is presented. Its definition is based on a complete direct imaging model developed by the authors. The application of this innovative algorithm to SAR images makes possible to obtain complete maps of the two key parameters of a fractal scene: the fractal dimension and the increment standard deviation. The fractal parameters extraction is based on the estimation of the power spectral density of the SAR amplitude image. From a theoretic point of view, the attention is focused on the retrieving procedure of the increment standard deviation, here presented for the first time. In the last section of the paper, the application of the introduced processing to high resolution SAR images is presented, with the relevant maps of the fractal dimension and of the increment standard deviation.

The increased amount of available Synthetic Aperture Radar (SAR) images involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In this paper we present the potentialities of TerraSAR-X (TS-X) data and Neural Network algorithms for oil spills detection. The radar on board satellite TS-X provides X-band images with a resolution of up to 1m. Such resolution can be very effective in the monitoring of coastal areas to prevent sea oil pollution. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The network output gives the probability for the candidate to be a real oil spill. Candidates with a probability less than 50% are classified as look-alikes. The overall classification performances have been evaluated on a data set of 50 TS-X images containing more than 150 examples of certified oil spills and well-known look-alikes (e.g. low wind areas, wind shadows, biogenic films). The preliminary classification results are satisfactory with an overall detection accuracy above 80%.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as landslide monitoring and digital elevation model extraction. One of its major limitation is the atmospheric effect, and in particular the high water vapour spatial and temporal variability which introduces an unknown delay in the signal propagation. However, the sensitivity of SAR interferometric phase to atmospheric conditions could in principle be exploited and InSAR could become in certain conditions a tool to monitor the atmosphere, as it happens with GPS receiver networks. This paper describes a novel attempt to assimilate InSAR derived information on the atmosphere, based on the Permanent Scatterer multipass technique, into a numerical weather forecast model. The methodology is summarised and the very preliminary results regarding the forecast of a precipitation event in Central Italy are analysed. The work was done in the framework of an ESA funded project devoted to the mapping of the water vapour with the aim to mitigate its effect for InSAR applications.

Detecting and monitoring of unstable slopes can greatly contribute to mitigate landslide hazards and reduce their adverse impacts. In this paper some application of SAR based multi-interferometric analysis applied to landslide studies in Alpine region is discussed, specifically the persistent scatterers (PS) identification useful to support landslide mapping and monitoring. Recently the PS interferometry (PSI) technique gained relevance, particularly where the implementation of traditional ground based measurements is too difficult or too expensive. Therefore, it is slowly evolving from a purely scientific application to an operational service, particularly appealing to those responsible for the management of geo-physical hazards and landscape management. The paper aims to present preliminary outcomes from a feasibility assessment of PS data analysis aimed to provide decision support to public officers. This objective requires deeper understanding and better communication of potentials and limitations of the PSI methodology, the various SAR sensors as well as of the ranges of displacement rate for that it can represent a suitable and reliable tool. This contribution is based on the work and preliminary results of two projects, namely the GMES Emergency Response Service SAFER (EC FP7) and "LAWINA" (COSMO SkyMed AO funded by the Italian Space Agency ASI).

A procedure for synthetic aperture radar (SAR) raw data generation for moving ships on the ocean is proposed, which combines the raw data simulation of the time evolving sea and the moving ships. The raw data of the ocean and the ship are simulated under the uniform coordinate system, respectively. The desired SAR signal is obtained by vector summation. For the ocean SAR raw data simulation, the dynamics and time-variant reflectivity function are taken into account. Moreover, an efficient and accurate algorithm with time-domain integration along range dimension is adopted. For the ship raw data simulation, the ship's six degrees of freedom movement driven by the time-varying ocean waves as well as the translation of the ship on sea surface are considered. Simulation results are presented to demonstrate the validity and applicability of the proposed techniques.

The current study evaluated the geometric accuracy of TanDEM-X twice in-situ by simultaneous observations using several corner reflectors. We set the reflectors on the flat ground, and measured the position of the reflectors before and after the satellite pass using GPS and achieved the accuracies within several centimeters. We utilized the orthorectified product which performs the correction of the geometric distortion. The results indicated that the geometric tendency of TanDEM-X is almost similar with TerraSAR-X. We also evaluated the features for correcting the geometric distortion by examining the relationships between the geometric accuracy and incidence angle of the satellites and noted that the more the incidence angle, the better the geometric accuracy proportionally. This evaluation revealed that we can actually acquire the outputs predicted by the theoretical model. The latest series of our conducted studies specify the high geometric accuracies and reliability of the specifications of the TerraSAR-X and TanDEM-X, the newest commercially available SAR satellites.