Polarimetric Synthetic Aperture Radar

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A Novel Field Scattering Formulation for Polarimetric Synthetic Aperture Radar: 3D Scattering and Stokes Vectors

A Novel Field Scattering Formulation for Polarimetric Synthetic Aperture Radar: 3D Scattering and Stokes Vectors

Abstract—Conventional Far-field decomposition of the scattered electromagnetic (EM) field in the [EH] plane in terms of the horizontal and vertical components (i.e., h, v), introduces ambiguity for multi- static, multi-platform and/or scene-centric polarimetric synthetic aperture radar (SAR) image exploitation. This is due to the fact that a 2-dimensional (2D) vector field can not constitute a complete space capable of modeling 3-dimensional (3D) field transformations. To address this, analytic extension of the Stokes and scattering vectors to 3D is explored and presented. In particular, coherent 3D polarimetric decomposition in Gell-Mann basis is introduced and explored as 3D generalization of standard Pauli decomposition. The results are also applicable to compact polarimetry (CP) where mathematically consistent 3D Stokes parameters can be defined.

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Independent Component Analysis Data Processing Framework for Polarimetric Synthetic Aperture Radar Images

Independent Component Analysis Data Processing Framework for Polarimetric Synthetic Aperture Radar Images

Abstract: The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (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.

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Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in the Canadian Arctic

Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in the Canadian Arctic

In addition to spectroscopy, synthetic aperture radar (SAR) is well known for its ability to characterize the physical surface properties of target rocks such as surface roughness and dielectric properties (e.g., Ulaby et al., 1982). Minimal or non-vegetation cover in the Arctic is ideal for SAR imaging on structures and physical surface properties as well as spectral mapping of rock surfaces. In particular, extensive glacial erosion and deposition can form different surface roughness depending on rock types (McCarroll and Nesje, 1996), which can be readily imaged using polarimetric SAR and utilized for geological mapping (Singhroy et al., 1992). SAR also has a great advantage for Arctic mapping because it is independent of sunlight and capable of penetrating clouds by transmitting its own source at relatively long radio wavelengths (Running et al., 1999). Spectral sensors, on the other hand, are disturbed by cloud coverage and very limited in their ability to acquire clear images for geological mapping. However, despite extensive developments of high-resolution polarimetric SAR systems in recent years, few workers have utilized polarimetric SAR capabilities for geological mapping. Several studies have conducted lineament feature extraction and reported on the variation in radar backscattering from different geological units (e.g., Graham and Grant, 1991; Saint-Jean et al., 1999; Smith et al., 1999). Even though some studies have tried to develop mapping algorithms by integrating SAR sensors with multispectral data, polarimetric SAR was used only as supplementary input parameters to improve statistical classification accuracy using variations in spectral and/or polarimetric SAR parameters (Grunsky, 2002; LaRocque et al., 2012; Pavlic et al., 2008; Y. Shelat et al., 2012a; Shelat et al., 2012b).

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Analysis of Polarimetric Synthetic Aperture Radar and Passive Visible Light Polarimetric Imaging Data Fusion for Remote Sensing Applications

Analysis of Polarimetric Synthetic Aperture Radar and Passive Visible Light Polarimetric Imaging Data Fusion for Remote Sensing Applications

have greater variance than in the DIRSIG image. However, the positions of the classes in the Pauli color space are almost same. For the single bounce effect, the dominant channel is the |HH+VV| thereby the points lie very close to the blue |HH+VV| channel for both the scenes. In case of the double bounce effect from the buildings in the scene, nearly most of the pixels for both the scenes lie in the blue |HH+VV| and red |HH-VV| plane in the plots for both the scenes (x-y plane in the plots in Figure 7.14). The multiple bounce effect has dominant |HV| channel hence the pixels lies on the |HV| axis in the color space (z-axis) in the plots. It can be observed that even though there are many differences in the scene, radar and image formation method still the distribution of the three basic scattering class in the Pauli Color space is basically same. It can be inferred that the analysis that will be done using the DIRSIG simulated images can be extended to real images from spaceborne and airborne PolSAR sensors.

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Application of Differential and Polarimetric Synthetic Aperture Radar (SAR) Interferometry for Studying Natural Hazards

Application of Differential and Polarimetric Synthetic Aperture Radar (SAR) Interferometry for Studying Natural Hazards

Differential Interferometric Synthetic Aperture Radar (DInSAR) is a technique for measuring ground deformation induced by natural or man-made processes (Massonnet & Feigl 1998; Bamler & Hartl 1998). DInSAR has the advantage of mapping an area of hundreds of square kilometers with high spatial and temporal resolution. The technique has been proved to be a powerful tool to measure deformation with centimeter accuracy over periods of days or years. DInSAR has been used for monitoring volcano dynamics (Massonnet et al. 1995; Manconi et al. 2010), coseismic displacements (Massonnet et al. 1993), subsidence due to exploitation of ground-water and oil/gas (Amelung et al. 1999) and mining subsidence (Carnec & Delacourt 2000). Recently this technique is used for monitoring of deformation associated with carbon sequestration and the melting of permafrost (Vasco et al. 2008; Short et al. 2012). Multi-baseline DInSAR technique have also been developed which are able to measure with milometer accuracy by using a larger number of SAR images (Sandwell & Price 1998; Feretti et al. 2001; Hooper et al. 2007; Berardino et al. 2002).

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Phase Statistical Model and Correction in Imagery of Ground Based Synthetic Aperture Radar (GBSAR) for Land Deformation Monitoring

Phase Statistical Model and Correction in Imagery of Ground Based Synthetic Aperture Radar (GBSAR) for Land Deformation Monitoring

The implementation of interferometry into radar signal processing first appeared in the late 1960s and early 1970s with the observation of Venus and the Moon’s surface. In 1974, Graham [5] first introduced interferometric techniques into synthetic aperture radar signal processing. The working principle of the Interferometric Synthetic Aperture Radar (InSAR) covers three primary stages. In stage one, two satellite-borne SAR imaging campaigns are conducted on the same area [10]. Next, the phases in the two SAR images are compared to produce the interferogram. In the final stage, the terrain elevation data are extracted and acquired from the phase information in the interferogram. In 1986, a side-looking airborne SAR system was installed on a NASA CV990 aircraft [13], and transmitted radar signal with one transmitter and received ground echo signal via two receivers simultaneously. In this experiment, they have successfully acquired data at San Francisco, California, USA. Two complex images of the region were processed to generate complex interferograms related to the terrain, and finally, a terrain heightmap with higher resolution was obtained.

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The Object Detection Efficiency in Synthetic Aperture Radar Systems

The Object Detection Efficiency in Synthetic Aperture Radar Systems

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 synthetic aperture radar (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 [7] 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 [8] 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

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Multifrequency and Multistatic Inverse Synthetic Aperture Radar, with Application to FM Passive Radar

Multifrequency and Multistatic Inverse Synthetic Aperture Radar, with Application to FM Passive Radar

It is first to be noted that relatively good image resolution is obtained with a small SAL when all transmitters are considered. This might mislead us into thinking that a small SAL is sufficient to obtain usable images of complex targets. Unfortunately, the sidelobe levels are high for these cases, as indicated by the MAE value. The MAE decreases as the SAL increases, which is reasonable. In particular, the result for an SAL of 32 km and 103 transmitters seems promising in terms of resolution and sidelobe levels. However, we are interested in reducing the number of transmitters to reach a less complex radar system; also, it is unlikely that so many transmitters could be used in practice.

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A Study on the Approaches and Algorithms for Classification of Synthetic Aperture Radar

A Study on the Approaches and Algorithms for Classification of Synthetic Aperture Radar

Jafari et al. introduced a new method for classifying polarimetric data in 2014 that can use polarimetric information in extra polarization bases. The base of this method is polarimetric signatures that are presented for different features in addition to power. Like other knowledge-based methods, the suggested method has two steps: 1. Source knowledge extraction, 2. Classifying using this knowledge. Three types of features are used here: complete covariance matrix, analytical values of H/a/A, and texture vector resulting from the co-occurrence matrix. Also, PCA algorithm is used to reduce feature space. The method is for extracting source knowledge of polarimetric signatures of different features. To classify through this method, correspondence methods are used. Also, to prevent noise output and for stability of polarimetric signatures in a piece object-based method is selected. The overall steps of the proposed method are demonstrated below:

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Search | Preprints

Search | Preprints

in the CWAVE_ENV model. Two parameters of the normalized radar cross section 𝜎 and the variance 𝑐𝑣𝑎𝑟, are directly calculated from the intensity data. The remaining 20 parameters are derived from the FFT spectrum of the ASAR WM intensity image. The major reason for using the spectral parameters is that the traditional nonlinear or quasi- linear retrievals connect the SAR image spectrum with two-dimensional ocean wave (or swell) spectra. On the other hand, 𝜎 is closely related with the wind speed (e.g., represented by the CMOD functions), and therefore, the information of windsea on SAR images is also involved in this equation. This is the general rational that the function can represent both swell and windsea information.

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Wavefront Reconstruction of Elevation Circular Synthetic Aperture Radar Imagery Using a Cylindrical Green's Function

Wavefront Reconstruction of Elevation Circular Synthetic Aperture Radar Imagery Using a Cylindrical Green's Function

Since its origins in 1951, Synthetic Aperture Radar (SAR) has been used for a wide variety of applications, from military reconnaissance to agricultural imaging to only name two examples [1]. Similarly to other radar imaging modalities, SAR techniques collect the reflections from an irradiated area and process them to create a reflectivity map from the scattering bodies present in the imaged region [2]. The SAR data acquisition process can be described as follows. A trajectory over the scan region is defined. Along this trajectory, an illuminating source radiates an ultra-wideband waveform and records the collected reflections from the objects inside the scan area. The recorded reflections are then processed to eliminate the distortions caused by the antenna, the shape of the irradiated waveform, and the motion of the moving platform [3–5]. Finally, the resulting reflectivity map can be visualized and interpreted.

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Synthetic Aperture RADAR (SAR) Implemented by Strip Map Algorithm

Synthetic Aperture RADAR (SAR) Implemented by Strip Map Algorithm

In modern pulsed Radar, range resolution (ΔR) isproportional to the pulse duration (τ). Therefore improvedrange resolution necessitates shorter pulse duration. Similarlythe energy (E) content of the signal is also proportional topulse duration (τ) and the detection probability dependsit.Therefore to improve the detection, the pulse duration isrequired to be longer. To overcome this two conflictingrequirements, pulse compression method is used. The pulse compression usually done through FrequencyModulation and Phase Modulation are very popular in radars[1].

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A New Factorized Backprojection Algorithm for Stripmap Synthetic Aperture Radar

A New Factorized Backprojection Algorithm for Stripmap Synthetic Aperture Radar

An alternative to direct backprojection is factorized back- projection. In factorized backprojection, the image re- construction is divided into a series of steps in which the resolution of the image becomes finer as the length of a synthetic subaperture increases. The geometry of the SAR array allows the interpolated radar data associated with the subapertures of the previous step to be used in subsequent steps, reducing the required computation at a tradeoff of some loss of accuracy.

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3D Imaging Millimeter Wave Circular Synthetic Aperture Radar

3D Imaging Millimeter Wave Circular Synthetic Aperture Radar

As the MMWCSAR system is producing the radar transmitted LFM waveforms. The received signal together with produced Gaussian noise is analyzed with IRT method and CS involved method. The simulation results is shown in Fig. 5 with FOV figure of 5 m from the radar panel. Fig. 5(a) addresses the IRT method with perfect setup without swinging inaccuracies. The velocity of radar is changing with evenly direction and the constant magnitude. Figures 5(b) to 5(g) introduce all the CS involved method by applying different sensing basis on Slow time-Angle bins separately. For experiment, we will use Equation (34) matrix to slow time profile like in Fig. 5(d) as it provides high contrast and better resolution.

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Recovery of the Target Image in the Presence of Translational Motion in Inverse Synthetic Aperture Radar Signal Processing

Recovery of the Target Image in the Presence of Translational Motion in Inverse Synthetic Aperture Radar Signal Processing

In the general case, the target may have both radial and rotational motion during the illumination period of radar. For this reason, the point scatterer at P(x, y) on the target is assumed to have both radial and rotational motion components. According to the practical convention of radar imaging, the phase center is selected in the middle of the target and is assumed to be the origin. We would like to estimate the phase error induced due to target motion. If the target is situated at the far field of the radar, the distance of point P from the radar can be approximated as-

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Ship Safety Management Using Mobile Applications with Cloud and Big Data Environment

Ship Safety Management Using Mobile Applications with Cloud and Big Data Environment

Synthetic aperture radar (SAR) is a radar technique that, in the remote sensing context, takes high-resolution images of the Earth’s surface from an aerial or space platform. It is operationally used in, among others, maritime surveillance applications, where it can detect non-cooperative vessels, during day and night, and through clouds. Space borne SAR sensors have a global coverage, although at any one moment, they can only monitor an area that ranges from a few tens to a few hundreds of kilometers wide. Additional shortcomings of spaceborne SAR are their long tasking and update times and the fact that most SAR satellites fly in the same dawn–dusk orbit, which limits the observation opportunities.

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Synthetic Aperture Radar (SAR) ImagesProcessing: A Review

Synthetic Aperture Radar (SAR) ImagesProcessing: A Review

The Synthetic Aperture Radar (SAR) is convenient for giving information about earth’s surface by using the respective motion between antenna and its target [1]. In various applications like automatic target detection, surface surveillance, mine detection etc. the SAR images provide very important information. In SAR imagery, one of the main problems is that the image textures are usually contaminated with multiplicative speckle noise which is due to coherent radiation in the process of imaging [2]. The texture present in the images usually contains important information about the scene. The objective of despeckling method is to remove speckle noise and to protect all textural features in the SAR images [3].

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An Introduction to Synthetic Aperture Radar (SAR)

An Introduction to Synthetic Aperture Radar (SAR)

In SAR system, there are basically three types of widely used modulation schemes: pulse, LFM or chirp and phase coded. Pulse system is used in older generation radar. Modern radar uses LFM waveform to increase range resolution when long pulses are required to get reasonable signal to noise ratio. The same average transmitting power as in a pulse system can be achieved with lower peakamplitude. The LFM configuration is employed in this project since it gives better sensitivity without sacrificing range resolution and ease of implementation. The lower peakpower allows for the use of commercially available microwave components that have moderate peakpower handling capability. Phase coded modulation is not prefer due to it’s difficulty to generate. Phase coded modulation normally used for long duration waveforms and when jamming may be a problem.

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Fundamentals of Synthetic Aperture Radar Systems

Fundamentals of Synthetic Aperture Radar Systems

In 1951, Carl Wiley improved the azimuth resolution by generating long antenna size based on signal processing not by using an actual physical antenna size. The idea was to synthesizing an antenna array by using a real aperture antenna mounted on a moving platform as shown in Fig. 5. The formation of synthetic aperture improves the azimuth resolution to be half of the antenna size in the direction of platform movement, without dependence on the distance between the targets and radar [1].

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Despeckling and Analysing Land Use Land Cover of Synthetic Aperture Radar Image

Despeckling and Analysing Land Use Land Cover of Synthetic Aperture Radar Image

significant contributions in this article are: for this suggested GGF-BNLM technique, the nonlinear weight kernel was effectively lowered and the guidance picture was effectively built using maximum probability rule and homogeneity analysis of local areas. Due to its usability in varying weather circumstances, SAR images are commonly used in separate areas. But due to the backscattered radar echoes interference, these are corrupted by multiplicative noise. These findings in the resolution degradation of SAR pictures, making it hard to analyze the SAR image, interpret it and process it. For a SAR picture, despeckling is therefore a main thing.

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