This dataset consists of integral sea state parameters of significant wave height (SWH) and mean wave period (zero-upcrossing mean wave period, MWP) data derived from the advanced syntheticapertureradar (ASAR) onboard the ENVISAT satellite over its full life cycle (2002-2012) covering the global ocean. Both parameters are calibrated and validated against buoy data. A cross-validation between the ASAR SWH and radar altimeter (RA) data is also performed to ensure that the SAR-derived wave height data are of the same quality as the RA data. These data are stored in the standard NetCDF format, which are produced for each ASAR wavemode Level1B data provided by the European Space Agency. This is the first time that a full sea state product in terms of both the SWH and MWP has been derived from spaceborne SAR data over the global ocean for a decadal temporal scale.
Radar signal processing with CS is addressed in [2,21,22,29–32]. In , Ender gave a full analysis on applying the CS to radar pulse compression. Sevimli  introduced Range-Doppler compressed sensing and optimization comparison of different reconstruction algorithms. In addition to the CS used in Range-Doppler response, our method of applying CS to radar signal processing is innovative. It allows not only the Pulse-Doppler compressed sensing, but also the Slow time-Angle compressed sensing. Recent work on CS applied to SAR systems is drawing researchers’ attention. For instance, Bao et al.  produced the 3D multi-circular SAR image using a "2D+1D" mode, i. e., 2D focused SAR images are followed by 1D profile estimation of the elevation direction. With CS applied 2D ground plane image and 1D profile of the elevation dimension, 3D figure can be reproduced. CS theory is also applicable in our MMWCSAR system. First, CS is applied to 2D Slow time-Angle data to form a 2D FOV image on a single range bin. Volume FOV figure can be reconstructed and analyzed by applying 1D range profile to 2D FOV images of different range bins. Second, to achieve the CS in radar signal processing, the sparsity and incoherence properties are discussed. In addition, 2D transformation from Slow time-Angle to Azimuth-Elevation representation matrix is discussed. Besides, we also introduce how to choose the sensing matrix, so that the CS algorithm can be realized. Finally, to further improve the performance of MMWCSAR system, we focus on decreasing the data acquisition time, improving imaging resolution and reducing errors caused by human with CS applied algorithm in experiment.
The stationary receiver/transmitter with moving transmitter/receiver has been consid- ered in various publications. In , a nonlinear chirp scaling is used to process bistatic ground based stationary receiver and moving transmitter operating in stripmap mode. The Range Cell Migration Correction (RCMC) is done in azimuth time domain. A non- linear chirp perturbation function is used to equalize the frequency modulation rates of targets at the same range gate. Bistatic interferometry using stationary receiver configu- rations is described in  for the generation of DEMs. A hybrid SAR processing method for bistatic stationary configurations is presented in . A Fourier based con- volution is used to partially process the data and residual errors are corrected by using time varying filter. In , a wave number domain algorithm is used to process station- ary transmitter with moving receiver configuration and results of simulated and real bistatic SAR data are provided. A sub-aperture range Doppler algorithm is used to pro- cess the stationary receiver configurations in . A stationary receiver system SABRINA (SAR Bistatic Fixed Receiver for Interferometric Applications) is used with a moving C-band SAR satellite (Envisat and ERS-2). For each transmitted pulse, a bistatic slant range map is calculated from DEM of the illuminated scene. The image is then divided into sub-apertures, the RCMC is performed in range Doppler domain and the azimuth compression is performed in two steps . It has been used for the interferometric applications in .
In Figure 11 four levels of radar observation are distinguished on the basis of increasing scale and decreasing observation frequency and coverage. At the lowest level, very frequent observations (several times per week) at medium satellite resolution are achieved, completely covering the area of interest (e.g. a whole country). Examples of systems at this level could be ESA’s ASAR (to be launched in 2000) in “Wide Swath” mode. A non-radar example is NOAA- AVHRR for fire detection, which is already operational in Indonesia. In “Wide Swath” mode ASAR could detect areas of special interest, which could be subsequently monitored at level 2, using high satellite resolution at a lower observation frequency (roughly once per month) to update mapping or detail selection of areas of interest. A lot of information has already been obtained using ERS SAR images, ranging, for example, from monitoring the expansion of the timber road network and the progress of strip cutting to the detection of small areas (less then 1 ha) of illegal clear-cut and fire damage. When proper use is made of level 2 information, airborne radar (or photography) flights can be planned much more efficiently. High resolution airborne radar can be efficiently applied to the detailed mapping of large areas (see also: Van der Sanden and Hoekman, 1999). An example is the “250,000 km 2 protected forest area” mapping campaign executed by Dornier in Indonesia in 1997. Similar images (from the INDREX campaign) were also found to be useful for the accurate checking of strip cutting and enrichment planting activities. Systems with longer wavelengths (L- and P-band) may be found very useful for mapping forest and land cover type, biomass and forest flooding. At the highest level, individual trees, rather than land cover units, are observed and short-wave, very-high resolution interferometric airborne SAR is required. It can be used in areas with legal logging activities and in areas of special interest identified through the first three levels of observation. On the other hand, where level 4 data are available, these could contribute to mapping accuracy by combining the information with that from level 3 mapping systems.
In this research, the most significant aim is to reduce the amount of raw data. We can use the concept of sparse array in the array sig- nal processing. The sparse array is a concept which is different from the ULA. The ULA has fixed distance between two adjacent sensors. However, the sparse array has different distance between different two sensors. This means that sparse array uses fewer sensors than the ULA in a same length of array. We can exploit the second order sta- tistical information to receive a smaller performance compared with the ULA. However, the amount of data decreases because the amount of sensors decreases. The co-prime array uses two co-prime integers in two sub-arrays. In Figure 3.10, for two sub-arrays with two co-prime integers, they share the same sensor in the first position and they will share the same sensor again until the M N − th position because M and N are co-prime. This means that we can generate an virtual array with consecutive integers from -M(N-1) to M(N-1), which is a larger virtual ULA with better performance. This is why we are going to utilize the co-prime array in SAR as well.
The employed measurement set-up is shown in Fig. 4. It consists of one horizontal and two vertical traversing units, on which a pair of antennas is mounted. The traversing units allow for a movement along a distance of approximately 1.1 m in the vertical direction and 0.65 m in the horizontal di- rection. It is therefore possible to span a syntheticaperture of those dimensions. The two vertical units can be moved sepa- rately, which also makes multistatic measurements possible. For the measurements presented in this paper we employed a quasi-monostatic set-up, i.e. transmitter and receiver antenna were in close proximity; they were mounted with a spacing of 2 mm between them. The antennas were two H-polarized horn antennas with a physical aperture of 2.45 mm × 4 mm (see Fig. 4). The transmitted signal was generated by a vector network analyser (Agilent PNA E8363B) with fre- quency extenders (Oleson Microwave Labs V10VNA2-T/R). A 201-point SFCW signal covering the complete W-band (frequency range: f min = 75 GHz to f max = 110 GHz) was
In order to understand the computational and memory requirements for SAR image processing, we have to choose a specific algorithm to analyze and implement. We have two main criteria for choosing the algorithm. First, the algorithm must have both test data and a reference implementation readily available. Second, the algorithm must be a good representative of SAR algorithms in general. The algorithm we choose is the Range Doppler Algorithm used in the RASSP project , which meets both criteria. It meets the first criteria by having quality test data from the M143 mission along with source code. Second, it is very representative of SAR algorithms because it is a variant of the Range Doppler Algorithm that is the core of many SAR processing algorithms. The algorithm is identical to the version of Range Doppler described in Section 2.1.2 with the exception that de-ramping is done as a part of the sampling process and as a result it is not included in our requirement analysis. The algorithm essentially consists of three major steps, converting the received signal to baseband, applying range compression and applying azimuth compression. They are explained in the following paragraphs.
Mapping of burn areas and severity has been previously performed using polarimetric syntheticapertureradar to varying degrees of success. An early attempt by Kasischke et al. (1992) looked at the double bounce component of the backscatter signal to determine burn area in black spruce forest, reasoning that there will be a high return from exposed ground and tree trunks due to defoliation relative to unburned areas. More recent studies have sought to utilize combinations of polarimetric SAR data to better reveal burn areas and severity. Some studies have used SAR data as a complement to optical data, by providing information where there might otherwise be gaps in the optical data due to obscuration and by enhancing the spectral image with more physical information (Bernhard et al. 2011; Bernhard et al. 2012). Another data fusion technique utilized UAVSAR data in conjunction with National Land Cover Database information, segmenting the entropy/anisotropy/alpha angle
Most scientists have shown great interest in the huge maritime environmental damage due to oil slicks, which have increase pollution effects greatly. Space-borne RADARSAT-1 SAR images are used to monitor and control oil slicks, however, the main challenges lies in the difficulties inherent in discriminating between oil spills and look–alikes. According to Maged and Hashim (2005) both appears as a dark spot in SAR data. Also according to Alpers and Hühnerfuss (1988); Trivero et al., (1998), the existence of an oil layer on the sea surface damps the small waves which increase the thickness of the top film and this significantly decreases the measured backscattering energy resulting in darker areas in SAR imagery. The European remote sensing satellite (ERS) task is an example of SAR.
On both TS-X and TD-X images, we can observe effect of rainfall on X-band SAR data, for instance, one can find some bright slicks in the large dark pattern area in both images. The rain band of Sandy is also clearly visible in the TD-X image (Fig. 6.14 (a)). In a previous study, Melsheimer et al.  investigate effects of rain cell on SAR imagery using SIRC/X-SAR data in multifrequency (L-, C-, and X-band) and multipolarization (HH, VV and HV). They summarized that the radar backscatter over ocean in the presence of rain cells is mainly associated with three processes: (1) scattering and attenuation of radar microwaves by hydrometeors in atmosphere, (2) the modification (enhancement or reduction) of sea surface roughness by rain drops, and 3) the enhanced sea surface roughness by wind gust. Generally, the latter two processes are easily identified in SAR imagery as both can significantly change the sea surface roughness. However, if the radar microwaves are attenuated by rain cell in atmosphere, it maybe not as clearly manifested in SAR image as other processes, particularly when rain fall has a large spatial coverage such as in hurricanes or typhoons. To quantify attenuation of radar backscatter induced by rain fall, Urlaby  proposed a radiative transfer model for C-band radar. Danklmayer and Chandra  present a model to quantify the attenuation for Ka- and X- band SAR. This two-way path attenuation model is given as:
In various fields of physics and engineering, processing should be implemented of the information signals observed against random interferences and in the conditions of various prior uncertainty [1–3]. The use of syntheticapertureradar (SAR) [4–6] makes it possible to rapidly detect the signal that arrives from the object surrounded by rough surfaces. The task of this paper is to theoretically study the efficiency of the optimal procedure  of inter period signal processing in the SAR when a target is detected against both reflections from the underlying surface (correlated interference) and the inherent noise of the receiver. It is intended to develop an accurate method for calculating the detection characteristics based on the calculation of the kernel of the characteristic function  and determine the conditions for achieving the specified values for both the false-alarm and the correct detection probabilities of the signals produced by the extended object situated within
Specifically, first the SRW distance image during a phase of digitization is digitalized to L gray degrees; the stage is required for the implementation of the Keatler and Ilingors committee of error algorithm and then the digitalized image will enter the process of thresholding so that the final map of the change or no change is gained through applying the error committee threshold as the output of the algorithm. Digital results of algorithm application on complete polarimetric simulated and real data shows that having more information from different polarizations in complete polarimetric images compared to single band images leads to higher unsupervised detection accuracy. Also, comparing the presented error minimum with improved evaluation method confirms high accuracy with minimum error of the algorithm proposed. Operation duration in this method is pretty short and since K&I uses image histogram in its algorithm, the algorithm running time does not depend on image size, and only a very minor difference in gaining the histogram may occur. Using test statistic cannot distinguish the type of changes resulted from increase or decrease of radar redistribution. The proposed method can detect the changes’ strength. Digital results and diagrams on conformity verification show the generalized Gamma distribution function used in this method can successfully model change and no change classes histograms for the polarimetric dada used.
In reality, for the past 30 years or so a large proportion of the innovation in radar systems arose from the use of digital technology in radar systems design, notably among them is in data processing. Indeed in recent years many radar designers have been switching — if not already switched — to the adopting of digital techniques that further contributed to the mushrooming of various digital techniques in SAR application all across the globe. This trend is expected to continue, with advances in radio frequency (RF) technology and antennas, besides digital technology. In brief, a digital world beckons. The digital approach taken to construct the chirp generator is limited to the state-of-the-art in digital components in the market. It relies heavily on the best components the market has to oﬀer, in terms of speed, precision and resolution (for instance, that of DAC). However digital circuits are also subject to the following limitations:
Both Cloude – Pottier 51 and Freeman – Durden 52 decompositions were also performed on the complex Radarsat-2 matrices. Cloude – Pottier (or eigenvector – eigenvalue) decomposition is based on the eigen decomposition of the coherency matrix (T3) into three matrices associated to orthogonal scattering mechanisms. Each of them denotes a scattering mechanism described by an eigenvector u i , and an intensity associated to the corresponding eigenvalue λ i . In order to analyze the physical information provided by the Cloude – Pottier decomposition, three param- eters were derived: entropy ( H ), which expresses the randomness of the polarimetric scattering process, alpha angle ( α ), which describes the mean scattering mechanism, and anisotropy ( A ), which represents the relative power of the second and third eigenvectors. Freeman – Durden decomposition is used to model the T3 matrix as the sum of three scattering mechanisms for each pixel: the volume scattering (Freeman VOL), which generally reflects a complex veg- etation canopy (randomly oriented dipoles), the double-bounce scattering (Freeman DB), which is characteristic of the incident wave interaction between a flat surface and a vertical object (dihedral corner reflector), and the surface or single-bounce scattering (Freeman SB), which corresponds to a flat or slightly rough surface.
3 technique for performing transactions in a distributed computing system. We propose to deploy Java-based mobile agents between a client and the SARA server, and use XML (eXtensible Markup Language) for encoding agent communication. Java’s platform independence, object serialization, multithreading, remote method invocation, secure execution, and dynamic class loading are essential properties for implementing a mobile agent system. XML is an industry- standard mechanism for tagging information between heterogeneous applications [XML]. XSIL [XSIL] is based on XML, and used to represent collections of scientific data objects, which can either be small objects with data explicitly contained in a file, or large objects represented by the salient metadata, with references to binary files elsewhere. XSIL is aimed at supporting, (1) Digital Puglia SyntheticApertureRadar Atlas, an archiving and processing facility for knowledge discovery in remote-sensing databases, (2) Digital Sky, a prototype confederation of astronomical surveys, (3) Center for Simulation of the Dynamic Response of Materials, a multi-disciplinary consortium at Caltech for simulations at multiple scales, and (4) Interferometric SAR digital library, a facility to improve the usability of the SyntheticApertureRadar Atlas.
analytical and numerical approaches. Results showed that multifrequency and multistatic radars could achieve suﬃ- cient resolution using several narrowband transmitters and a long integration time. In this paper, we propose imaging algorithms for multifrequency and multistatic radars, which could be useful as a preprocessing for classification methods. Most of imaging algorithms are based on the SyntheticApertureRadar (SAR) concept . The main purpose of imaging algorithms is to achieve enough resolution to make the classification step easy. For instance, the final images corresponding to two similar targets must exhibit enough di ﬀ erences so that it is possible to make the di ﬀ erence. For narrowband radar, it is well known that data from transmitter-receiver pairs distributed all around the target allow to have an image [11, 12] with such properties. These particular systems are similar to tomographic ones. Recently, Wu and Munson  simulated the imaging of a moving airplane using a multistatic radar composed of several TV transmitters and a single receiver. The imaging algorithm in  relies on the assumption that the target is composed of isotropic points and uses 2D interpolation on the frequency domain to achieve the processing. However, the resulting images are subject to degradation because the target has to be illuminated over a long period (in this case, the target is often nonstationary). Several methods have been proposed for solving this problem: time-frequency- based methods [14–16], deconvolution methods , or optimization techniques .
Rapid growth in the volume of data produced by satellite SAR sensors over the last decade has stimulated the development of new methods for automatic analysis of SAR images. Several approaches have been developed in the literature for the detection of IWs in SAR data. Rodenas and Garello  used a 2-D spectral analysis based on short-time Fourier transform (STFT) to study IW packets in ERS-1 SAR images and to estimate packet wavelength. A similar technique was applied by Changbao et al.  to evaluate the dominant wavelength and propagation direction. Rodenas and Garello  suggested that the wavelet transform (WT) can perform better than STFT in locating irregularities of the IW signal. They used an analytical IW model to develop a set of wavelet basis functions that were adapted to the IW signatures and applied these functions for analysis of IW patterns in ERS-1 SAR images of the Strait of Gibraltar. The approach based on WT was further extended and a fully automatic tool was developed for detection and orientation estimation of IWs in the ERS-1 SAR images in . The tool employed decomposition of SAR images into different scales using 2-D dyadic WT to suppress speckle noise and improve the accuracy of edge detection. The output of the multi-resolution edge detector was further processed to discriminate look-alike features such as ship wakes, oil slicks and currents. Rodenas and Garello  demonstrated the efficiency of this method by processing individual ERS-1 and RADARSAT SAR scenes.
ABSTRACT: In this system, we tend to use huge information with final server storage capability. Our datasets indicate the GeoSpace nature, which can be regenerate into compression mean by suggests that of SVM classifier and trimming operate. The abstraction question possibility is employed for looking out the resultant information from the large information server. The advance in Maritime Situational Awareness, the potential of understanding events, circumstances, and activities inside and impacting the maritime atmosphere, is today of predominant importance for safety and security. the mixing of area borne artificial aperture radiolocation (SAR) information and automatic identification system (AIS) data has the appealing potential to supply a much better image of what's happening embarrassed by police work vessels that aren't reportage their positioning information or, on the opposite facet, by confirmative ships detected in satellite mental imagery. During this approach, we tend to propose a unique design that's able to increase the standard of SAR/AIS fusion by exploiting information of historical vessel positioning data. Experimental results square measure conferred, testing the rule within the specific space of capital of Delaware Strait exploitation real SAR and AIS information. For all the entire system deals with manipulation of GeoSpace Dataset with two powerful classification algorithms to predict the future analysis of the marine vessels, the algorithms are C4.5 and Support Vector Machine (SVM).
Abstract: The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric SyntheticApertureRadar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee’s sigma filter to the particular nature of the Touzi’s polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.