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This paper describes a new high performance cost effective Earth Observation Platform, the SSTL-300 and associated optical sensor suite, offering a 7-year mission lifetime with a very high operational availability, for a mission cost an order of magnitude less than commercial high resolution Earth observation spacecraft. The paper will detail the SSTL-300 main payload, a very high-resolution imager with panchromatic multispectral channels. The paper will also outline an additional payload, the Medium Resolution Imager (MRI), which offers the widest swath of any comparable Earth Observation spacecraft. Earth images can be geolocated with high accuracy without the need for ground control points. Simultaneous high- and medium- resolution imaging is possible, coupled with either on-board lossless data compression for store-and-forward operations or near real-time imaging & down-linking for a range of targets close to the ground station. The SSTL-300 offers a range of imaging modes, including: strip mapping, fast response scene capture and stereo imaging offering swath widths up to 60 km at high-resolution. Details of the nominal (sun-synchronous) orbit for the SSTL-300 will be provided stressing the balance between single spacecraft and constellation (multiple cooperating spacecraft) performance. The latter requires an accurate and reliable, yet low cost propulsion system which SSTL has developed, but is commonly featured on low cost small spacecraft.

This paper addresses the main goals and objectives of the Hyperspectral Imaging Network (HYPER-I-NET), a recently started Marie Curie Research Training Network. The project is designed to build an interdisciplinary research community focusing on hyperspectral imaging activities. The core strategy of the network is to create a powerful interdisciplinary synergy between different domains of expertise closely related to hyperspectral imaging activities in Europe, ranging from sensor design and flight operation to data collection, processing, interpretation, and dissemination. Our main goals in this paper are to present the project to the Geoscience and Remote Sensing community and to provide an overview of the planned activities in each sub-activity covered by the network.

This paper deals with a feasibility analysis of spaceborne bistatic missions for Earth Observation, with signals at microwave bands. The analysis, performed in the frame of a wider study financed by ESA, considers as input bistatic configurations, investigated in another paper, suitable for typical land applications i.e. soil moisture and vegetation biomass retrieval. Signal sources are GPS constellation and spaceborne SAR's. This analysis mainly encompasses the design of possible orbits implementing the desired bistatic configurations, and the characterization of the system performances. The latter are evaluated through several different parameters, grouped in two classes: target observation parameters, strictly related to the illuminator-target-receiver geometry at given epoch (e.g. signal to noise ratio, spatial resolution, observation angles) and system parameters such as: spatial coverage (measuring whether or not a point is accessible by the satellites in bistatic configuration), number of bistatic accesses to the target area, revisit time (indicating the delay between two successive bistatic acquisitions of the same area).

This paper presents a distributed approach for time- domain focusing, which significantly enhances the overall efficiency by distributing the computational load across a (potentially large) number of networked computers. The system described includes the so-called master, responsible for pre-processing, the distribution of fragments of raw-data data and the collection of processed image fragments. Fragments of raw-data are passed to so-called slaves, any number of which can be connected to the master, which are responsible for the focusing itself. Master and slave actively communicate over the network to organise the entire process in a scalable manner. In this way, time-domain processing can be accelerated by a factor that is virtually linear in the number of participating slaves. This paper summarises the current status of software development, realised in a platform-independent way using the IDL and Java languages. Additionally, some preliminary evaluations of performance, scalability and the required network infrastructure are given. Some examples of SAR data, acquired by the airborne sensor E-SAR of DLR, and processed with the system described are shown.

Recent research has demonstrated the value of polarimetric measurements for the correction of rain-path attenuation at X-band radar frequency and the estimation of rain parameters including drop-size distributions (DSD). The issue this paper is concerned with is to what degree uncertainties in attenuation correction can affect the estimation of DSD. Since attenuation-correction uncertainty enhances with rain path, our hypothesis is that DSD retrieval uncertainty at X-band may deteriorate with range. In this paper, we evaluate the relative accuracy of X-band DSD retrieval against DSD estimates from S-band radar observations and in situ disdrometer spectra. We present comparisons of various techniques for estimating DSD model parameters from attenuation-corrected X-band dual-polarization radar data. Coincident X-band polarimetric-radar (XPOL) and S-band polarimetric-radar dual-polarized radar measurements from the International H2O Project experiment as well as coincident XPOL (MP-X) measurements over disdrometer during a typhoon storm case in Japan are used to assess the accuracy of the different DSD retrieval algorithms applied to X-band radar measurements.

One of the advantages of Frequency Modulated Continuous Wave (FMCW) radars is the relative low required sampling frequency even when transmitting high bandwidths. This is a consequence of the inherent homodyne configuration of the radar front-end. A simple way to reduce further the data rate is to use few bits per sample. The paper analyzes the effects of low bit sampling in FMCW Synthetic Aperture Radar (SAR) data. A low number of bits used to sample the FMCW deramped signal could produces spurious peaks and intermodulation products of the sinusoidal signal. However, it is shown in the paper that, for typical value of signal to noise ratio in FMCW systems, white noise suppresses the spurious products. Simulation results and verifications on real data collected with the FMCW SAR system built at the Delft University of Technology are presented.

This paper is devoted to describe the activity carried out by CRPSM(Centro di Ricerca Progetto San Marco) in the framework of the SIGRI(Italian acronym for Integrated System for Fire Risks Management) project. This project aims to develop a system, based on satellite data, able to support operationally the activities of users like Italian Civil Protection Agencies or Fire Dept. involved in fighting wild fires. In particular, the system should be able to support all the phases in which a fire fighting activity can be distinguished, namely: Territory management and resources dislocation (fire risk indices), fires detection and monitoring, damage assessment (burned areas and emissions in atmosphere). This paper presents the results obtained in the process of assessing the quality of a fire hazard forecast based on a fire potential index especially designed for the Mediterranean areas. This quality assessment is carried out comparing the daily computed indices with the fire distribution obtained by using a fire detection algorithm based on SEVIRI/MSG images.In fact, using a fire detection algorithm (SFIDE, System for Fires Detection), recently proposed by the authors, a despite of its low spatial resolution,the SEVIRI system is able to reveal, at latitudes corresponding to Italy,fires covering an area of the order of 0.1 ha. The fire potential index (FPI) is one of the most suitable to be computed by using satellite data even if ancillary information are still needed. The computation of this index requires the estimate of the relative greenness, the evaluation of the leaves humidity, the preparation of vegetation fuel maps. Among the parameters needed to compile this index the fuel type map is particularly crucial. In fact, accurate maps of this kind are not available for the Italian territory. Then, first of all, using Corine Land Cover and other available vegetation maps, medium resolution satellite images and "in situ" observations CRPSM carried out the development of these maps for a couple of Italian regions where the summer wild fires problem has higher incidence.

This paper deals with SAR data segmentation in an unsupervised way. The model we propose is a combination of the nonstationary triplet Markov field recently introduced and the Fisher distributions. The first one allows modeling the different stationarities present in a given image. The second one has the advantage that is well adapted to this kind of data. We present an original technique based on Iterative Conditional Estimation method, to estimate the parameters of the model we propose. Application examples on simulated data and real SAR images are presented as well.

Thanks to their capability to cover large areas, in all weather conditions, during the day as well as during the night, spaceborne Synthetic Aperture Radar (SAR) techniques constitute an extremely promising alternative to traditional surveillance methods. Nevertheless, in order to assure further usability of SAR images, specific data mining tools are still to be developed to provide an efficient automatic interpretation of SAR data. The aim of this paper is to introduce texture analysis performed in the framework of time - frequency theory, as a means to detect oil spills in the sea surface. In particular, an algorithm permitting a precise quantitative characterization of the border between the oil spill candidate and the sea, will allow a novel classification of oil spills and look-alikes.

As TerraSAR-X, due for launch in February 2007, will be an operational scientific mission with commercial potential, product quality is of paramount importance. The success or failure of the mission is essentially dependent on the calibration of the TerraSAR-X system ensuring the product quality and the correct in-orbit operation of the entire SAR system. The essential task of calibrating TerraSAR-X is to estimate and correct systematic error contributions throughout the complete SAR system and to tie-down image information (magnitude and phase) to reference units in geophysical terms. The quality of this calibration process is dependent on the inherent stability of the radar system and the capability to determine and monitor the radiometric and geometric characteristics. In addition to the geometric calibration the main calibration activities are defined by the radiometric accuracy and the radiometric stability specified for the operational modes. Due to the high degree of flexibility of the instrument, e.g. the large number of operation modes (StripMap, Spotlight, ScanSAR, left/right looking, etc.) and possible antenna beams, the in-orbit calibration procedure of TerraSAR-X is based on a novel antenna model approach. In addition to the complexity of TerraSAR-X and the tight performance requirements, the main goal after launch of the satellite is to provide calibrated and verified SAR data products as soon as possible. Thus, a strategy for an efficient but robust in-orbit calibration procedure has been developed. For this purpose, the novel antenna model developed and validated on ground, is verified in orbit by measuring only a number of selected beams using deployed ground receivers and natural targets (e.g. the rain forest). Absolute calibration, yielding an absolute calibration factor by measuring the radar system against reference ground targets, requires precise and sophisticated ground equipment. To account for the restricted time of calibration campaigns the number of passes and places of test sites is optimised versus cost and time effort by calibrating several beams and polarisation modes with the same test site. Therefore, corresponding test site configurations have been developed. The evaluation of measurements performed during the calibration campaigns yields the different calibration parameters like the antenna pattern or the absolute calibration factor. The paper describes the in-orbit calibration procedure and the different activities performed during the commissioning phase of TerraSAR-X and discuss first results.

The geosynchronous imaging Fourier transform spectrometer (GIFTS) sensor module (SM) engineering demonstration unit (EDU) is a high resolution spectral imager designed to measure infrared (IR) radiances using a Fourier transform spectrometer (FTS). The GIFTS instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The raw interferogram measurements are radiometrically and spectrally calibrated to produce radiance spectra, which are further processed to obtain atmospheric profiles via retrieval algorithms. This paper describes the processing algorithms involved in the calibration stage. The calibration procedures can be subdivided into three stages. In the pre-calibration stage, a phase correction algorithm is applied to the decimated and filtered complex interferogram. The resulting imaginary part of the spectrum contains only the noise component of the uncorrected spectrum. Additional random noise reduction can be accomplished by applying a spectral smoothing routine to the phase-corrected blackbody reference spectra. In the radiometric calibration stage, we first compute the spectral responsivity based on the previous results, from which, the calibrated ambient blackbody (ABB), hot blackbody (HBB), and scene spectra can be obtained. During the post-processing stage, we estimate the noise equivalent spectral radiance (NESR) from the calibrated ABB and HBB spectra. We then implement a correction scheme that compensates for the effect of fore-optics offsets. Finally, for off-axis pixels, the FPA off-axis effects correction is performed. To estimate the performance of the entire FPA, we developed an efficient method of generating pixel performance assessments. In addition, a random pixel selection scheme is designed based on the pixel performance evaluation.

The Institut Cartografic de Catalunya (ICC) operates a digital metric camera (DMC), manufactured by Z/Imaging. It supports the simultaneous capture of panchromatic, RGB colour and near-infrared images. One of the most important ICC products is the Catalonia orthophotomap series. This product is intended to depict the territory as realistically as possible. With this aim, ICC is developing a project to obtain genuine colours in the photographic products produced with the DMC camera and this paper contains some preliminary results. Since an absolute radiometric calibration of the DMC is not available yet, this is accomplished through simultaneous images acquired with both DMC and a compact airborne spectral imager (CASI) also operated by ICC. These images are used to locate common areas imaged in similar geometrical conditions, and then a linear relationship between digital numbers (DN) from DMC and radiance values of a CASI image emulating DMC bands is calculated. To improve the colour of the DMC products, a colorimetric calibration based on polynomial transformations has been developed. This transformation allows changing from DMC-RGB space to a sRGB space that is based on CIE-XYZ colour space. Airborne ICC-CASI images acquired with a Cessna Caravan B20 over Banyoles (Spain) in 2005 were used to test the methodology. The comparison between the training colours and the ones obtained after colorimetric calibration yield differences in 8 bit DN that range from 4 to 36 DN with a mean value of 12 DN. The effect was also visually analyzed on different subsets of images that include agriculture areas, urban landscape and a natural water layer. Finally, it was found that the application of both calibrations to the DMC images results in a consistent colour combinations for all the analyzed subsets.

In this paper, we describe the implementation of a distributed data retrieval and processing strategy enabled by using grid computing technologies and applied to distributed collaborative adaptive sensing environments. Underlying the grid computing and storage infrastructure there is a data dispersion algorithm to guarantee pervasive data management. Experimental results show that the proposed framework integrates successfully radar networks to grid infrastructures, while providing higher resources utilization than typical storage strategies.

金沢大学大学院自然科学研究科知能情報・数理, This paper introduces a method for cloud classification using NOAA AVHRR satellite images. AVHRR (Advanced Very High Resolution Radiometer) data consists of five-channel multi-spectral images. To reduce the dimensionality of the data, principal component analysis (PCA) is calculated for each channel separately. The most significant principal component values are then composed into an image feature vector. Finally, the feature vectors are clustered using self-organizing map (SOM). This method is applied for the study of winter season clouds in the Japan Sea area. © 2007 IEEE.

This paper presents an efficient phase preserving processor for TOPS (Terrain Observation by Progressive Scans) imaging mode. TOPS has been proposed as a new wide swath imaging mode, which solves the problems of scalloping and azimuth-varying ambiguities introduced by the conventional ScanSAR mode by means of steering the antenna along the azimuth direction. An algorithm based on ECS (Extended Chirp Scaling) is proposed, which uses sub-apertures and a new azimuth scaling step. The proposed solution is also efficient in the sense that it allows selecting the final azimuth image spacing by means of azimuth scaling, hence easing the forthcoming mosaicking of the different sub swaths. Simulations with point targets are used to validate the processor.

TerraSAR-X is a high resolution synthetic aperture radar (SAR) satellite due for launch in 2007. Its active phased array X-Band antenna hosts 384 transmit/receive modules controlling the beam steering in azimuth and elevation direction. Precise modelling of the antenna is only possible if the actual characteristics of each individual transmit/receive module are known. TerraSAR-X has been equipped with an innovative characterisation mode based on the so-called PN Gating method. Individual and simultaneous characterisation of all transmit/receive modules is realised under most realistic conditions with the same power loads like in the nominal mode. This paper shows the results of PN Gating measurements on a satellite SAR system.

This article presents early results of the FUDOTERAM project using bathymetric LiDAR data acquired with the SHOALS-3000, the latest bathymetric LiDAR system from Optech. The survey area is in the coastal zone along the northern shore of Chaleurs Bay, in the western Gulf of St. Lawrence, Canada. The project aimed to apply the SHOALS- 3000 to geological mapping, sedimentary process monitoring and marine habitat mapping. This paper focuses on the sedimentological part of the study and presents the early raw data obtained to produce a bottom type classification based on some simple parameters, roughness, slope angle and direction. Two methods are evaluated for analysis of the SHOALS-3000 waveforms, the Moment Method and the Gaussian Mixture Model, and the latter is used as an approach to model the bottom type signal.

SAR tomography (SARTom) is an imaging technique that allows multiple phase centers separation in the vertical (height) direction. It is performed after standard 2D SAR repeat-pass processing and operates on a stack of coregistered SAR images. Theoretically, the coregistration between two images is height dependent and the use of a reference height (or a DEM) is needed, although not ideal in the case of volumetric target (multiple phase centers in one resolution cell). In this paper, the drawbacks related to the choice of this reference in a tomographic context are analysed and a height dependent coregistration approach is proposed. In order to do this, it is also necessary to remove processing corrections related to the reference height, such as motion compensation, and make them height dependent. The inclusion of the height dependency during the tomographic SAR processing results in a better quality of the final tomograms in terms of pseudo-power and phase centers separation. The results of the proposed approach are validated on real data acquired by the E-SAR system of the German Aerospace Centre - DLR.

The geosynchronous-imaging Fourier transform spectrometer (GIFTS) engineering demonstration unit (EDU) is an imaging infrared spectrometer designed for atmospheric soundings. It measures the infrared spectrum in two spectral bands (14.6 to 8.8 mum, 6.0 to 4.4 mum) using two 128times128 detector arrays with a spectral resolution of 0.57 cm-1 with a scan duration of ~11 seconds. From a geosynchronous orbit, the instrument will have the capability of taking successive measurements of such data to scan desired regions of the globe, from which atmospheric status, cloud parameters, wind field profiles, and other derived products can be retrieved. The GIFTS EDU provides a flexible and accurate testbed for the new challenges of the emerging hyperspectral era. The EDU ground-based measurement experiment, held in Logan, Utah during September 2006, demonstrated its extensive capabilities and potential for geosynchronous and other applications (e.g., earth observing environmental measurements). This paper addresses the experiment objectives and overall performance of the sensor system with a focus on the GIFTS EDU imaging capability and proof of the GIFTS measurement concept.

A current challenge in performing airport obstruction surveys using airborne lidar is lack of reliable, automated methods for extracting and attributing vertical objects from the lidar data. This paper presents a new approach to solving this problem, taking advantage of the additional data provided by full-waveform systems. The procedure entails first deconvolving and georeferencing the lidar waveform data to create dense, detailed point clouds in which the vertical structure of objects, such as trees, towers, and buildings, is well characterized. The point clouds are then voxelized to produce high-resolution volumes of lidar intensity values, and a 3D wavelet decomposition is computed. Vertical object detection and recognition is performed in the wavelet domain using a multiresolution template matching approach. The method was tested using lidar waveform data and ground truth collected for project areas in Madison,Wisconsin. Preliminary results demonstrate the potential of the approach.

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.

This paper presents airborne differential SAR results using a stack of 14 images, which were acquired by the experimental SAR (E-SAR) system of the German Aerospace Center during a time span of only two and a half hours. An advanced differential technique is used to retrieve the error in the digital elevation model and the temporal evolution of the deformation for every coherent pixel in the image. The two main limitations in airborne SAR processing are analyzed, namely the existence of residual motion errors (inaccuracies in the navigation system in the order of 1-5 cm), and the accommodation of the topography and the aperture during processing. The SAR focusing chain to process the data is also presented, together with the modifications in the differential processor to deal with the remaining baseline error. The detected deformation of a corner reflector and of several agricultural fields allows validating the proposed techniques.

Polarimetric SAR interferometry (Pol-InSAR) is a powerful remote sensing method for forest height estimation by using the random volume over ground model (RVoG). At higher frequencies implementation of forest height estimation in X band is limited to less dense and low forest types where X band is able to penetrate through the volume to the ground. However, the penetration depth at X band is insufficient to cover all forest types. In the paper, forest height inversion at X band using two different approaches is demonstrated with focus on the impact of extinction on forest height estimation.

Flood extent maps derived from SAR images are a useful source of data for validating hydraulic models of river flood flow. The accuracy of such maps is reduced by a number of factors, including changes in returns from the water surface caused by different meteorological conditions and the presence of emergent vegetation. The paper describes how improved accuracy can be achieved by modifying an existing flood extent delineation algorithm to use airborne laser altimetry (LiDAR) as well as SAR data. The LiDAR data provide an additional constraint that waterline (land-water boundary) heights should vary smoothly along the flooded reach. The method was tested on a SAR image of a flood for which contemporaneous aerial photography existed, together with LiDAR data of the un-flooded reach. Waterline heights of the SAR flood extent conditioned on both SAR and LiDAR data matched the corresponding heights from the aerial photo waterline significantly more closely than those from the SAR flood extent conditioned only on SAR data.

An alternative interpolation technique for SAR image coregistration in interferometric processing is formulated and described. The proposed algorithm is based on the 1- D Farrow interpolator and, when combined with an adequate implementation, it involves a smaller computational burden than the conventional method and yields high accuracy. Basically, this technique enables to carry out the coregistration without resorting to a set of functional weights, and it only requires arithmetic operations and 2-D FFTs. This paper includes several results and comparisons that confirm its validity: the proposed technique is combined with two different polynomial interpolation procedures (Taylor and Chebyshev); bounds on its interpolation error in the 1-D and 2-D cases are derived; and, additionally, it is tested numerically with a synthetic image.

Synthetic aperture radar data presents specific problems for interactive visualization. The high amount of multiplicative speckle noise has to be reduced. The high dynamic range of the amplitude data must be mapped to the lower dynamic range of display devices in a way that makes image features appropriately visible. In addition to interactive navigation in the data, it is desirable to allow interactive selection of despeckling and dynamic range reduction methods and adjustment of their parameters. Graphics processing units (GPUs) can be seen as ubiquitous parallel coprocessors with extreme computational power. In this paper, we propose a GPU-based framework for interactive visualization of SAR data. Data management techniques are used to make full use of the GPU. We reworked well-known despeckling and dynamic range reduction techniques for the GPU programming model and implemented them in our framework. Both navigation in large data sets and adjustment of processing parameters are fully interactive.

For the 3D reconstruction inside historic buildings, we need a marker-free automatic registration approach to align different views together, because GPS does not work indoors and markers are not allowed to paste on the walls. This paper presents an automatic matching process, which employs a novel algorithm, Dynamic Matching Tree technique, for a fast and stable coarse-matching to achieve the automatic pre-alignment of two point clouds and uses modified ICP to do a fine matching efficiently. The whole process can be divided in the following stages: preprocessing, 2-View matching and N-View matching. To validate our method, various experiments has been done on reconstruction of historic sites and industrial objects.

Soil secondary salinization is one of the typical ecological side effects caused by land and water resources development in northwestern arid China. Factors that affect the occurrences and developments of salinization come from both natural conditions and human activities. Research on the mechanisms of salinization, build dynamic prediction model of salt accumulation and analyze sensitivities to different factors would supply effective references to the prediction and prevention of soil salinization. It is well known that related factors are always intertexture together, affecting each other, which result in multivariable, nonlinear and overall influences that work on the process of soil salinization. Artificial intelligence technologies may play important role in this domain. In this paper, genetic artificial neural network based model is built to simulate and evaluate soil salt accumulation and sensitivity of soil salinization. Example is taken from the Shule River watershed, typical arid area in northwestern China. Basic data of June 2000 are prepared depending on GIS and Remote Sensing. Precipitations, evaporations, groundwater levels, groundwater chemical analysis data and soil accumulation data are achieved and interpolated in the research area. Slope of the land are derived from DEM, MODIS images are used in the process of dealing with land use information. At the same time, landform and soil type are considered in model building. Soil salt accumulation is analyzed with its 8 influenced factors with verified models. Results showing that groundwater TDS is the most sensitive factor followed by groundwater level, evaporation and the depth of upper bed of clay. In most cases clay layers play key roles in soil salt accumulation, precipitation and slop have similar sensitivities. Results would have better research and application value in arid areas of northwestern China.

The fractal geometry proved to be the most appropriate mathematical instrument in describing natural scenes, by means of few effective and reliable geophysical parameters. In this paper we use fractal concepts to model and to identify geometrical changes occurred in areas hit by disasters. We present an overall framework employing fractal based models, algorithms and tools to support identification of natural area changes due to natural or man-made disasters. The proposed framework includes a SAR raw signal simulator, which is of key importance to improve the comprehension of the mechanisms underlying SAR image formation. In addition, we consider, as a case study, the simulation and detection of lava flows in a volcanic scenario. The potentialities of our technique for the discrimination between different types of lava are presented and discussed.

ALOS, an enhanced successor of the Japanese Earth Resources Satellite 1 (JERS-1), was launched from JAXA’s Tanegashima Space Center in January 2006. ALOS operates from a sun-synchronous orbit at 691 km, with a 46-day recurrence cycle carrying a payload of three remote sensing instruments: the Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) , the Advanced Visible and Near-Infrared Radiometer type 2 (AVNIR-2) and the polarimetric Phased Array L-band Synthetic Aperture Radar (PALSAR). The PALSAR sensor has the capacity to operate with a wide range of off-nadir angles and resolutions in a single-, dual-, and quad-pol mode. An important contribution to the ALOS mission is the verification of PALSAR products to be distributed by the European ADEN node using the PALSAR processor developed by JAXA. Under contract to ESA/ESRIN the Microwaves and Radar Institute at the German Aerospace Center (DLR) Oberpfaffenhofen and its partners BAE Systems Advanced Technology Centre and CREASO GmbH developed a quality assessment tool (CALIX) for the verification of standard ALOS PALSAR products and also provided support for the verification for the ALOS PALSAR data quality. In addition, a set of corner reflectors and transponders have been deployed at the DLR test site Oberpfaffenhofen throughout the whole commissioning phase to allow an accurate measure of the basic product quality parameters. The quality assessment features of the CALIX software include a product reader, antenna pattern estimation, point target analysis, distributed target analysis, geometric analysis and polarimetric analysis. Results obtained with CALIX on a set of products acquired over the DLR test site will be presented in this paper. Since PALSAR is the first fully polarimetric spaceborne L-band sensor, propagation effects are important new issues to be addressed. The main challenge is to assess the influence of the ionosphere on the polarisation. Different approaches for estimating the Faraday rotation angle have been applied and the results are here compared.

International audience; Automatic image interpretation could be achieved by first performing a segmentation of the image, i.e. aggregating similar pixels to form regions, then use a region-based classification. This paper presents two region-based classifications, namely a supervised classification and an ontology-based classification and discuss their pros and cons. As they are complementary, we propose to combine these two approaches. Results shown that the presented method is relevant.

The paper deals with the problem of reconstructing the height of forests from polarimetric/multi-baseline SAR data. The approach consists of optimizing an objective functional defined as the distance between the measured data and the data predicted by the model at the actual estimate of the unknowns. We indicate the role of global optimization on the performance of the forest height reconstruction algorithm. As global optimizer, a multilevel single-linkage method, which incorporates a local optimization into the global search, is exploited, thus offering computational efficiency and reliability. The performance of the method are illustrated against numerically simulated data.

Brazilian Amazon is a vast territory rich in natural renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation, poor access), topographic information is still poor, and when available needs to be up-dated or re-mapped. In this paper, the feasibility of using altimetric information for topographic mapping through orbital stereoscopic (ASTER, RADARSAT-1) and interferometric (SRTM-3) DEMs (digital elevation models) was investigated for two regions in the Brazilian Amazon: Tapajos National Forest (flat terrain) and Serra dos Carajas (mountainous relief). The quality of information produced from these data was evaluated regarding field altimetric measurements. Precise topographic field information acquired from differential global positioning system (DGPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DEMs and as independent check points (ICPs) for the calculation of altimetric accuracies of the products. The investigation has shown that the accuracy of the altimetric information derived from Fine RADARSAT-1, ASTER and SRTM-3 DEMs met the requirements for a semi-detailed (1:100,000-map) scale as requested by the Brazilian standard for cartographic accuracy. Furthermore, SRTM-3 DEM was more accurate than stereoscopic DEMs. The additional great advantage of using SRTM-3 is the free access data. However, up-dated planimetric information is also necessary for cartographic production. Thus it is suggested a combination of altimetry derived for SRTM-3 and planimetry from high-resolution SAR (Fine RADARSAT-1, PALSAR) or if possible optical data (ASTER, SPOT) for topographic mapping at semi-detailed scale in similar environments of the Brazilian Amazon, where terrain information is seldom available or presents low quality.

This paper presents the top-level design of an airborne, P-band ice sounding radar under development at the Technical University of Denmark. The ice sounder is intended to provide more information on the electromagnetic properties of the Antarctic ice sheet at P-band. A secondary objective is to test new ice sounding techniques, e.g. polarimetry, synthetic aperture processing, and coherent clutter suppression. A system analysis involving ice scattering models confirms that it is feasible to detect the bedrock through 4 km of ice and to detect deep ice layers. The ice sounder design features a digital signal generator, a microstrip antenna array, a conventional RF-architecture with a central transmitter, four receivers, and internal calibration loops. In 2008 the first data acquisition campaign will take place in Greenland.

Airborne repeat-pass SAR systems are very sensible to sub-wavelength deviations from the reference track. To enable repeat-pass interferometry a high-precision navigation system is needed. Due to the limit of accuracy of such systems, deviations in the order of centimeters remain between the real track and the processed one causing mainly undesirable phase undulations and misregistration in the interferograms, referred as residual motion errors. Up to now only interferometric approaches, as multisquint, are used to compensate for such residual errors. In this paper we present for the first time the use of the autofocus technique for residual motion errors in the repeat-pass interferometric context. A very robust autofocus technique has to be used to cope with the demands of the repeatpass applications. We propose a new robust autofocus algorithm based on the WLS (Weighted Least-Squares) phase estimation and the Phase Curvature Autofocus (PCA) extended to the range-dependent case. We call this new algorithm WPCA (Weighted PCA). Differently from multisquint, the autofocus approach has the advantage of being able to estimate motion deviations independently leading to better focused data and correct impulse response positioning. As a consequence better coherence and interferometric phase accuracy are achieved. Repeat-pass interferometry based only on image processing gains in robustness and reliability since its performance does not deteriorate with time decorrelation and no assumptions need to be made on the interferometric phase. Repeat-pass data of the E-SAR system of the German Aerospace Center (DLR) are used to demostrate the performance of the proposed approach.

Segmentation of dark patches in SAR images is an important step in any oil spill detection system. Segmentation methods used so far include 'adaptive image thresholding', 'hysteresis thresholding', 'edge detection' (see [1] and references therein) and entropy methods like the 'maximum descriptive length' technique [2]. This paper extends and generalizes a previously proposed Bayesian semi-supervised segmentation algorithm [3] oriented to oil spill detection using SAR images. In the base algorithm on which we build on, the data term is modeled by a finite mixture of Gamma distributions, with a given predefined number of components, for modeling each one of two classes (oil and water). To estimate the parameters of the class conditional densities, an expectation maximization (EM) algorithm was developed. The prior is an M- level logistic (MLL) Markov Random Field enforcing local continuity in a statistical sense. The methodology proposed in [3] assumes two classes and known smoothness parameter. The present work removes these restrictions. The smoothness parameter controlling the degree of homogeneity imposed on the scene is automatically estimated and the number of used classes is optional. To extend the algorithm to an optional number of classes, the so-called alpha-expansion algorithm [4] has been implemented. This algorithm is a graph-cut based technique that finds efficiently (polynomial complexity) the local minimum of the energy, (i.e, a labeling) within a known factor of the global minimum. In order to estimate the smoothness parameter of the MLL prior, two different techniques have been tested, namely the least squares (LS) fit and the coding method (CD) [5]. Semi-automatic estimation of the class parameters is also implemented. This represents an improvement over the base algorithm [3], where parameter estimation is performed on a supervised way by requesting user defined regions of interest representing the water and the oil. The effectiveness of the proposed approach is illustrated with simulated SAR images and real ERS and ENVISAT images.

The Brazilian Amazon is an area where extensive tropical rainforest areas are being converted to agriculture and cattle raising activities, contributing to environmental and landscape changes of this large region. The main objective of this paper is test whether spatial patterns of deforestation as visualized by satellite images are related to land use data as collected by household survey. This linkage would allow for a better understanding of the dynamic of land use and cover change at a regional scale. The results demonstrated the capacity of our methodology in quantifying and mapping deforestation patterns and to relate these patterns to land use activities at the household level.

The objectives and methodology of the MAMUD research project are presented in this paper. MAMUD is an acronym for Measuring and Modeling Urban Dynamics: Impact on Quality of Live and Hydrology. The research will be conducted over a four year period (2007-2011). The major goal is to investigate how earth observation can contribute to a better monitoring, modeling and understanding of urban dynamics, and its impacts on the urban and suburban environment.

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (end member signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the end-members signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on independent component analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

In this paper, we will present well controlled experimental bistatic X-band measurements of rough surfaces, which have been recorded in the Bistatic Measurement Facility (BMF) at the DLR Oberpfaffenhofen, Microwaves and Radar Institute. The bistatic measurement sets are composed of soils with different statistical roughness and different moistures controlled by a TDR (Time Domain Reflectivity) system. The BMF has been calibrated using the Isolated Antenna Calibration Technique (IACT). The validation of the calibration was achieved by measuring the reflectivity of fresh water. In the second part, the first validation of the specular algorithm by estimating the soil moisture of two surfaces with different roughness scales will be reported. Additionally, a new technique using the coherent term of the Integral Equation Method (IEM) to estimate the soil roughness will be presented, as well as evaluation of the sensitivity of phase and reflectivity with regard to moisture variation in the specular direction.

This paper formulates a new approach for ambiguous surface clutter suppression for ice sounding radars. While surface clutter from the non-orthogonal direction is reduced by means of Doppler and/or SAR processing, the residual surface contributions from across-track may still mask the reflection of internal layers and the bedrock, both coming from the nadir direction. The suggested approach involves several repeated acquisitions separated by certain baselines. It uses a geometry model to derive a synthesized sparse array diagram with adaptive nulls in the direction of the ambiguities. Initial performance evaluations for a P-band space-borne mission indicates that clutter can be suppressed by 30-40 dB. The approach can potentially be demonstrated with airborne sounder data in case precise navigation and motion compensation is ensured.

A new temperature controlled microwave cavity system to measure the complex dielectric constant of seawater at 1.413 GHz is discussed. The system is being developed to measure seawater for temperatures from 0degC to 30degC and salinities from 10 to 40 psu. The paper discusses the construction of the measurement system and initial stability tests.

Oceanographic applications using satellite altimeter data become very challenging when leaving the deep ocean for the coastal regions. Close to the coast, altimeter observations are often of lower quality for a number of reasons, including land contamination of the satellite footprints or inaccurate resolution of the corrections of the high frequency ocean response to tidal and atmospheric loading. This paper presents a new processing toolbox, called X-TRACK, to derive improved altimeter products, such as sea surface heights (SSH), mean sea surface heights (MSSH) or sea level anomalies (SLA), dedicated for coastal applications. Starting from classical geophysical data records (GDR) products, particular attention is made to recover a maximum amount of exploitable data (dedicated data editing, interpolation of missing corrective terms). Where possible, local modelling of the high frequency response of the ocean to the tidal and atmospheric loading is applied instead of standard, global corrections given in the GDRs. In addition, orbit errors are reduced by a stability criterion and a high resolution mean sea surface consistent with the altimeter data set is computed along the satellite ground track. This poster presents promising results obtained in various coastal areas.

Off-shore detection of tsunami waves is a critical component of an effective tsunami warning system (TWS). Even more critical is the off-shore detection of local tsunamis, namely tsunamis that strike coastal areas within minutes from the triggering quake. In this paper we propose a new concept for tsunami detection. NESTRAD (Near-Space Tsunami Radar) consists of a real aperture radar accommodated inside a stationary stratospheric airship providing continuous monitoring of tsunamigenic oceanic trenches.

A Bayesian segmentation approach for hyperspectral images is introduced in this paper. The method improves the classification performance of discriminative classifiers by adding contextual information in the form of spatial dependencies. The technique herein presented builds the class densities based on fast sparse multinomial logistic regression and enforces spacial continuity by adopting a multi-level logistic Markov-Gibs prior. State-of-art performance of the proposed approach is illustrated in a set of experimental comparisons with recently introduced hyperspectral classification/segmentation methods.

Although SeaWinds was not originally designed to observe tropical cyclones, new higher resolution products resolve much of the horizontal structure of these storms. However, these higher resolution products (reported at 2.5 km) are inherently noisier than the standard 25 km products and the high rain rates often associated with hurricanes corrupt the wind estimates. Fortunately, these storms have structure which can be exploited using a model. This paper develops a new procedure for hurricane wind field estimation from the SeaWinds instrument at ultra high resolution. We develop a simplified hurricane model to provide prior information to be used in maximum a posteriori probability (MAP) wind estimation. Using the hurricane model ameliorates the effects of rain and noise on the scatterometer measurements and directly provides useful hurricane parameters such as the eye center location and intensity.

Rotylenchulus reniformis nematodes present in the soil are one of the major nematode parasite species significantly affecting the growth and development of cotton plants. Recent studies have shown that the nematode numbers in the plant's rhizosphere has direct impact on the reflectance of the plants. In this paper, authors utilize this correlation in developing a field worthy methodology for predicting nematode population number extant in the plant's rhizosphere from variable plant's reflectance. To accomplish this task, a supervised Self-organized map (SOM) was trained using the hyperspectral data signatures of cotton plants affected by different known nematode numbers. The hyperspectral signatures used for training were collected from the cotton plants grown in controlled environment. Twelve field samples (uncontrolled environment) with known nematode numbers obtained from lab analysis of the soil were presented to the supervised trained Self-Organized Map. The location of the sample on the labeled supervised-SOM was used to determine the estimated nematode population of the field sample. In addition to the map grid, the locations of the samples were also visualized using U-matrix, to determine whether the samples were not corrupt or located in the junk part of the map. In addition to the primary goal, hyperspectral signatures of both training and testing data were divided into three sub-regions: Visible region, NIR region and Mid-IR region to observe whether any particular region was the most effective in predicting nematode population.

In this paper we assess the influence caused by the lack of atmospheric correction of the optical images used to calculate land surface emissivity (LSE). When thermal emissivity is determined by the Vegetation Cover Method (VCM), information from the solar spectrum in order to calculate the vegetation cover fraction is required. The atmospheric correction is performed in this research by using the combination of the Dark Dense Vegetation method (DDV) and the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) code. The methodology was applied to a Landsat TM image of Tomelloso, Spain. It was determined that the emissivity between 10 and 12 mum only increases 0.4% (which represents a systematic error of approximately +0.2 K in temperature) when atmospherically corrected reflectances are used in relation to non-corrected scenes.

Geographic Information Systems (GIS) and Remote Sensing (RS) applications are becoming an important issue for the territorial management, governmental and research projects, and for many fields of our society. A characteristic of such applications is the displaying of successive layers of information that, in some cases, may overlap areas of the displayed images that are eventually never showed to the final user of the application. Even though these overlapped areas are of null interest, the coding of these images considers the complete area of the image, and thus the coding performance of the compression system is penalized. This paper introduces a novel use of the Region Of Interest (ROI) coding techniques to overcome the drawbacks of the map overlapping in GIS and RS applications. The proposed approach is based on a ROI coding method defined for the JPEG2000 standard that efficiently improves the coding performance and keeps JPEG2000 compliance.