Noise radar can be used in a great number of applications including SAR. The non-periodic waveform suppresses the range ambiguity and reduces the probability of intercept and interference. Due to the randomness of the waveform, a noise floor limiting the possible side lobe suppression accompanies the correlation integral involved. In strong clutter scenes with dominant reflectors, the induced noise floor can be too high and further suppression is needed. In this paper, the ambiguity function of random noise waveforms is first analyzed, and an improved formulation is introduced to include the noise floor effect. The use of mismatched filtering for improved sidelobe suppression is then discussed. Finally, an iterative subtraction algorithm is analyzed for noise floor cancellation in the presence of dominating reflectors. This method is successfully tested on random step frequency radar data and noise sodar data.

In this contribution, we present a study on a series of representations of polarimetric synthetic aperture radar (SAR) data, testing and comparing them with respect to their utility for land cover classification. Different classification algorithms are also compared. Part of this work is dedicated to the study of the dependence of the classification results on the varying size of averaging windows of pixels. Such an analysis will permit to prove if the polarimetric parameters under consideration describe only point-like physical properties of the targets or if they also contain "extended" local information. The final goal is to provide an objective estimate of the usefulness of these parameters.

In this paper the actual capabilities of ENVISAT/ASAR images in providing soil moisture maps have been tested. Several SAR images were collected on two test areas: a flat agricultural region in the Alessandria area, in Italy, and the natural area of Kemijoki river system, in Finland. An inversion algorithm based on Artificial Neural Networks (ANN) for the retrieval 4-5 levels of soil moisture from backscattering data was tested and successfully compared to ground measurements.

Information on terrain features like slope, orientation and convexity may be very useful for thematic interpretation of single band satellite radar data. Accuracy of the co-registration is the key issue. The map-projected GBFM radar mosaic of Siberia has been co-registered with a digital elevation model, using a cross-correlation technique in the Fourier domain. The mosaic was produced in the framework of the Global Boreal Forest Mapping Project, an initiative of the Japan Agency for Space Exploration (JAXA), and is based on JERS-1 synthetic aperture radar (SAR) data acquired in years 1997-8. The SRTM digital elevation data (90 m horizontal resolution) have been used for areas up to 60 degrees of latitude and the USGS GTOPO30 elevation data (500 m horizontal resolution) for the rest of the area. Since SAR and DEM data-sets capture completely different features of the landscape and SAR imagery is affected by geometric and radiometric (shadow and layover) distortions due to elevation and local terrain slope, automatic matching by homologous features of the radar image to the DEM image is not possible. Due to the unavailability of SRTM data at the time of the mosaic processing and, in any event, due to computational constraints (the mosaic is composed of some 400 SAR strip-images covering 135 000 km2 each) the classical geo-coding procedure using slant range data had to be ruled out. The a-posteriori solution entailed the simulation of the radar reflectivity dependency on the local incidence angle based on available DEM and radar viewing geometry. The radar mosaic was then matched with the simulated image. The cross-correlation moving window was composed of mutually overlapping squares (60 by 60 pixels) in a regular grid with 20 pixel spacing between the centers. The co-registration gives good level of correlation not only for mountainous areas but also for hilly ones. High correlation occurs also in flat areas with pronounced hydrological features like river courses and lake shores that are reflected in SRTM as fine-detail features. The density of control points was on average 1400 points per 100 square kilometers. The geometric effect of topography (like shortening of the slopes oriented towards the radar) has been then corrected by inversion of the same model which had been used for generating the simulated image. Radar backscattering coefficient dependency on local incidence angle was modeled and corrected by a simple inverse sine function model. The corrected radar image was then fed to a classification algorithm together with layers extracted from the DEM, such as slope and convexity. Preliminary thematic classification results confirm that geometric and radiometric corrections afforded by this technique greatly improve the classification accuracy.

One major concern in the retrieval procedure lies in the procedure for its validation. This procedure implies a comparison between retrieved values and in-situ measurements. Even in the ideal case the ground and satellite measurements are fundamentally different, since the ground data can be sampled continuously in time but at a discrete point, while a satellite samples an area average but a snapshot in time. In this paper a retrieval algorithm for the estimation of soil moisture from AirSAR, acquired on vegetated fields during the SMEX'02 experiment carried out in Iowa in 2002, is presented. The retrieval procedure, based on a Bayesian approach, consists of two modules, one is pertinent to bare soils while the other one is useful for the determination of soil moisture in presence of vegetation. The last one uses the synergy with optical images to correct for the contribution of vegetation water content. The abovementioned campaign has been chosen because, along with radar observations, extensive ground truth measurements were acquired. The main aim of the paper is to investigate how well an average value of the ground variable, such as soil moisture, that is obtained by inverting the satellite data, corresponds to a simple average of the same variable over a finite region determined from ground truth data. This is what happens when satellite derived measurements are compared with point measurements.

Interferometric Synthetic Aperture Radar (InSAR), one of typical application techniques of SAR data, is used to generate Digital Elevation Model (DEM) while it is also used to measure spatial surface deformation. The former and the later techniques are usually differentiated as InSAR and Differential InSAR respectively. In this paper, we focus the later technique as InSAR. We measured ground deformation using automatic InSAR processing and verified accuracy of obtained results. The study area is Zonguldak coalfield, located along the Black Sea coast approximately 240 kilometer away from Istanbul to the east, Republic of Turkey. In this region, underground coal mining has been undergoing since 1848 and 3 million ton per year of hard coal has been produced. Recently, this coal mining is found to be causing subsidence around this area. Since the whole damage has not been grasped, we tried to measure the amount of surface deformation using InSAR. As a result, some phase anomalies were detected just above mining drifts, and the largest deformation amounts among those was 204 millimeter per 4.5 months in slant range direction. In addition, InSAR results are corresponded with GPS measurement results within 9 millimeter variations.

Due to upcoming new data driven technologies in the aviation the impact of digital terrain data is growing conspicuously. Especially for ground near operations reliable terrain information is necessarily demanded. Based on modern earth observation technologies a new generation of elevation data is obtainable. However it shall be analysed how far data derived from remote sensing techniques like INSAR or LIDAR can be applied for aviation purposes. Formerly terrain data were represented in relation to the bare earth to obtain a "Digital Terrain Model" (DTM). For aviation purposes a "Digital Surface Model" (DSM) representing the real surface of the earth including all cover like vegetation and buildings is recommended. Due to the characteristics of active remote sensors the derived model always describes an in between of the two elevation representations. To satisfy the special requirements the Institute of Flight Systems and Automatic Control (FSR) at the Technische Universitaet Darmstadt is dealing with the determination of the influencing factors which affect the quality of the terrain models being appropriate to be used as a DSM. In order to enhance the integrity of the data a "safety buffer" is created to allow the applicability for dedicated applications (figure 1).

Synthetic aperture radar (SAR) is an advanced remote sensor with abilities of all-weather, day-and-night observation and can be represented as various polarimetric aspect. Due to the coherent imaging of SAR, it is always with the drawback of speckle. In order to further use SAR image for applications, the speckle reduction is an essential task. Lee Filter is a very famous filter to reduce speckle in a SAR image. The particular characteristic of Lee Filter is that it employs eight different direction non-square windows to preserve the edge sharpness of SAR image. However, it is still insufficient to preserve the edge sharpness in the area whose land-cover has irregular shape. This study is trying to combine the K-means clustering, Hopkins index to construct filtering window that has more exact shape to approach the land-cover for better preserving the edge sharpness after speckle reducing. The pilot results show that the filtering scheme mentioned above can perform better edge-sharpness preserving. But the filter efficiency and the parameters decision need further study to find out the optimal setting.

The utility of ASAR data will be greately enhanced if the Radiometric and Geometric quality of the data satisfies the requirements of applications. 7 secenes of ASAR data acquired during April 2004 - June 2005 have been processed to assess the Geo-location accuracy and also temporal / spatial variability of backscattered signatures. Ground Crontrol Points (GPS) approach was used for Geometric accuracy assessment. 80 GCPs spread all through the image were measured using Trimble 5700. It is estimated that the geometric accuracy of the data is within 55 m. This is in agreement with the reported accuracies in the literature. About 20 well defined targets were considered for the backscattering signature study. The size of each target varies from 1000 to 5000 SLC pixels for good statistical stability of the signature. It has been estimated that the Standard Deviation (STD) of the sitgnature is 1.3% and the temporal / spatial variability of the signatures ( considering all varieties of targets such as built-up area, grass lands, airports, agricultureal plots, desert soils, etc) are within its STD. During this study period the soil moisture varied from 10% to 6% as recorded from AMSR-E on board Acqua satellite.The ASAR data has been filtered for speckle noise.

The Cassini spacecraft, launched in 1997, begun the exploration of the Saturnian system in July 2004. 44 flybies of the Saturn largest moon Titan are planned during the four years of the Cassini mission and 16 are Radar passes. A Radar, developed jointly by JPL, ASI and Alenia Spazio, is mounted on Cassini. The instrument operates at 13.8 GHz (Ku band) and has passive (radiometer) and active (scatterometer, altimeter, SAR imaging) capabilities. Until July 2006, six (Ta, T3, T7, T8, T13, T16) radar planned passes have been accomplished. The data are processed by the JPL and stored in the Basic Image Data Records (BIDR) files, thus obtaining SAR images and brightness temperature profiles of a significant fraction of Titan's surface. The Radar Cross Section, RCS, derived from the SAR imagery, reflects the complex Titan's surface morphology. The data show RCS variations in excess of 20 dB between the "brightest" and the "darkest" areas. On the basis of brightness, texture, and morphology eight different surface units were identified by Cassini Radar Science Team (CRST) scientists. The darkest features are good candidate to be lakes of hydrocarbons. Moreover, periodic structures ("sand dunes") have been observed: in this case the RCS variations can be described in terms of tilt angle effect, thus modifying the local incidence angle. In this paper, the RCS behaviour of the observed features is studied in detail by the means of the Integral Equation Method, IEM. The dependence of the backscattering coefficient on the surface physical properties, composition and roughness of different areas is analyzed resulting in some possible scenarios for the observed features. Surfaces are modeled as Gaussian stationary processes and volume scattering is also taken into account, due to the transparency of water ice at radar wavelength. The data of the IEM model are compared with numerical simulations based on the Kirchhoff approximation with auto similar surfaces performed as preparatory work in the preliminary stage of the Cassini mission. The RCS data simulated for some likely scenarios of Titan's surface are well consistent with the real radar data and can help their interpretation in terms of physical and morphological surface properties.

Remote sensing systems, such as SAR usually apply FM signals to resolve nearly placed targets (objects) and improve SNR. Main drawbacks in the pulse compression of FM radar signal that it can add the range side-lobes in reflectivity measurements. Using weighting window processing in time domain it is possible to decrease significantly the side-lobe level (SLL) of output radar signal that permits to resolve small or low power targets those are masked by powerful ones. There are usually used classical windows such as Hamming, Hanning, Blackman-Harris, Kaiser-Bessel, Dolph-Chebyshev, Gauss, etc. in window processing. Additionally to classical ones in here we also use a novel class of windows based on atomic functions (AF) theory. For comparison of simulation and experimental results we applied the standard parameters, such as coefficient of amplification, maximum level of side-lobe, width of main lobe, etc. In this paper we also proposed to implement the compression-windowing model on a hardware level employing Field Programmable Gate Array (FPGA) that offers some benefits like instantaneous implementation, dynamic reconfiguration, design, and field programmability. It has been investigated the pulse compression design on FPGA applying classical and novel window technique to reduce the SLL in absence and presence of noise. The paper presents simulated and experimental examples of detection of small or nearly placed targets in the imaging radar. Paper also presents the experimental hardware results of windowing in FM radar demonstrating resolution of the several targets for classical rectangular, Hamming, Kaiser-Bessel, and some novel ones: Up(x), fup₄(x)•D₃(x), fup₆(x)•G₃(x), etc. It is possible to conclude that windows created on base of the AFs offer better decreasing of the SLL in cases of presence or absence of noise and when we move away of the main lobe in comparison with classical windows.

The main idea of proposed method is based on the novel possibility to create a precise point action of explosion series which produces the diffraction grating. In practice this process could be achieved by bombarding the ocean with explosive material in vicinity of tsunami region from the earth or from airplane. The tsunami velocity could be decreased and the wave front could be disturbed. In this paper the relevant calculations were performed for this overall process to be optimized.

Modeling of the electromagnetic (EM) scattering and synthetic aperture radar (SAR) images of a ship over sea surface is a great challenge due to the extremely complicated scattering mechanisms between the complex target and the dynamically variable sea surface. In this work, we propose an approximate but practical technique for high-frequency EM scattering prediction and SAR image modeling of ships over sea surface, where major scattering mechanisms from both the ship's hardbody and the multipath interaction between the ship and the sea surface are considered. Computational examples for radar cross section (RCS) and SAR images are presented, demonstrating the validity and usefulness of the current technique.