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Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

Geometric features are commonly used for classification in controlled environments such as integrated circuit manufac- ture plants, where the position and orientation of the object with respect to the light source are known [13]. The proposed ATR system for sonar uses the same type of geometric features but in a less controlled environment where the target can present any aspect to the sonar and the signal-to-noise ratio is much smaller. To do so, 24 geometrical features have been selected to differentiate a set of targets. The features are computed from the segments extracted in the detection stage (Section 3). Nine features concentrate on the highlight area, twelve focus on the shadow, and three extract information from the low backscatter area in between them. The low backscatter area is defined as the region that is limited in the range direction by a highlight segment (with pixel values above a given threshold t h ) and a shadow segment (below t s ,
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Interferometric Synthetic Aperture Sonar Signal Processing for Autonomous Underwater Vehicles Operating Shallow Water

Interferometric Synthetic Aperture Sonar Signal Processing for Autonomous Underwater Vehicles Operating Shallow Water

My goal is to present a summary of basic ocean physics to lay the foundation for Interferometric synthetic aperture sonar (InSAS) processing in the shallow water acoustic environment. Ocean acoustic models derived from the previously mentioned physics will be used to predict the acoustic response of the sonar in a large set of ocean environments. Beamforming signal processing techniques are presented to give a basic understanding of what is involved in converting the changes in ocean pressures at the sonar face into meaningful digital electronic data to form images of the ocean bottom. The details of standard practices for synthetic aperture sonar will be presented to build the framework for three dimensional bathymetric imaging. The thrust of this investigation will be to
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Synthetic aperture sonar imaging via one-way wave equations

Synthetic aperture sonar imaging via one-way wave equations

In this paper we discuss the migration method based on one-way wave equations [1, 2] for Synthetic Aperture Sonar (SAS) imaging [3]. The one-way wave equation integrates the data within a given angle and minimizes the undesirable effects of unwanted reflections. Efficient and stable integration methods of the one-way wave equation based on the operator splitting method are used to develop a fully discretized algorithm. The stability analysis and the required operation count of the proposed algorithm are given. We test the proposed method for real field data and report our SAS imaging results. We also discuss the image enhancement method for the resulting images, based on BV-like regularization technique [5]. In side-scan (side-looking) sonar systems a platform containing a moderately large real aperture antenna travels along a rectilinear path in the along track direction and periodically transmits a pulse at an angle that is perpendicular to the platform path. These systems produce strip-map (SAS) images . A strip-map image is built up as follows; the imaging system operates such that the echoes from the current pulse are received before the next pulse is transmitted. As these echoes are received they are demodulated, pulse compressed, and detected (only the magnitude information is retained). Each detected pulse produces a
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Applying Image Fusion and Fuzzy Clustering Algorithms to detect Changes in Synthetic Aperture Radar Images

Applying Image Fusion and Fuzzy Clustering Algorithms to detect Changes in Synthetic Aperture Radar Images

Image change detection is a process that analyzes a pair of images which are acquired on the same geographical area at two different time instants. It has found important applications in remote sensing [1], flood monitoring, geographical map updating [2], medical diagnosis and video surveillance [3]. Remote sensing systems offers many advantages out of which one is the capability of repetitive coverage of a particular area at different times to study change detection process. Different types of sensors can be used to accomplish this task. Synthetic aperture radar (SAR) imagery [4] has found a major field of interest when compared to optical ones, due to its advantages such as operating in various weather conditions and are not affected by different sunlight conditions and cloud cover etc., but these SAR images contain some unwanted artifacts in the form of granules which is called as speckle noise. Due to the presence of this speckle noise, some of the crucial and important information of the images may be lost. In spite of its drawback change detection is clearly done due to its advantages. Many algorithms have been already proposed to detect the changes in SAR images. In this paper two aspects
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Terahertz inverse synthetic aperture radar imaging using self-mixing interferometry with a quantum cascade laser

Terahertz inverse synthetic aperture radar imaging using self-mixing interferometry with a quantum cascade laser

lows the creation of high resolution images by applying a coherent-sum process to measured scattered signals in such a way that the scattered signals are synthetically “back-propagated” to the target surface. In the last few years, synthetic aperture imaging techniques have been adapted to the THz spectral region. However, most of this work is applied at frequencies below 1 THz using microwave-based technologies (e.g. Ref. 16). In contrast, Danylov et al. 17 develop an experimentally-complex THz

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Speckle Reduction of Synthetic Aperture Radar Images using   Median Filter and Savitzky Golay Filter

Speckle Reduction of Synthetic Aperture Radar Images using Median Filter and Savitzky Golay Filter

SAR images are corrupted by speckle noise which is based on multiplicative noise or Reyleigh noise. The speckle degrades the quality of image and makes interpretations, analysis and classifications of SAR images harder. Therefore some speckle mitigation is necessary prior to the processing of SAR images. In this paper a new method is proposed for despeckling of SAR images in which Savitzky-Golay filter and median filter are used for denoising of the synthetic aperature radar (SAR) image. After obtaining filtered image they are decomposed by the use of undecimated wavelet transform. The speckled input image is also decomposed using undecimated wavelet transform. Then image segmentation is done by the use of brute force thresholding wavelet based algorithm in which each pixel of the entire decomposed image is compared and the maximum value of threshold image pixel is replaced in every iteration of image processing. Lastly enhanced directional smoothing of the image is done to obtain a despeckled image
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Advanced Methods For Building Information Extraction From Very High Resolution SAR Data To Support Emergency Response

Advanced Methods For Building Information Extraction From Very High Resolution SAR Data To Support Emergency Response

The advantage of SAR imagery is its relative insensitivity to atmospheric conditions and independence from sun illumination. SAR data availability shortly after an event is thus, in principle only based on the SAR sensor’s orbiting characteristics, i.e. the sensor’s revisit capability. The COSMO-SkyMed sensor constellation currently has three satellites, allowing to revisit a target every 2-3 days, while TerraSAR-X has a revisit time of about 11 days. Unsupervised change detection methods using multitemporal SAR data have been proposed in [9], [16], and [118]. Methods utilizing the interferometric coherence to detect damages were proposed and demonstrated in [119] for the earthquake example in Kobe (Japan, 1995), in [120] for the Bam (Iran, 2003) earthquake and in [121] for the Izmir (Turkey, 1999) earthquake. Damage assessment methods for urban areas using medium and/or high resolution pre- and post-event SAR images based on changes in the backscatter coefficient and intensity correlation were proposed in [122] for the 2004 Indonesia earthquake, and in [123] and [124] for the Kobe earthquake. The latter approach is tested in [125] for the Bam and in [126] for the 2003 Algeria earthquakes. Methods for the generation of damage maps using pre- and post-event SAR and VHR optical imagery were proposed in [127] for the Bam earthquake. Hybrid methods using SAR in combination with GIS layers were proposed for the Bam earthquake in [10], for the Algeria and 2007 Peru earthquakes in [128], and with respect to the high seismicity of Tehran, Iran, in [129]. Terrain surface changes after the recent 2008 Sichuan, China, earthquake were investigated in [130] analyzing the difference image of multitemporal ALOS PALSAR data. A study about the appearance of damaged bridges in SAR was presented in [131].
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Hydrocarbon Lakes on Titan and Their Role in the Methane Cycle

Hydrocarbon Lakes on Titan and Their Role in the Methane Cycle

changed in the summer of 2009, when the RADAR obtained is first images of Ontario Lacus. Ontario Lacus, Titan’s largest southern lake, was originally observed by the ISS in- strument in 2004 and 2005 [Turtle et al., 2009a]. It was also observed by the VIMS instru- ment in late 2007, which produced spectra that identified liquid ethane in its composition [Brown et al., 2008]. In the RADAR images, Ontario Lacus looked smaller than it did in 2005. This was confirmed by lower resolution ISS images from March 2009 [Turtle et al., 2011]. By combining the RADAR images with altimetry data obtained the previous De- cember, we were able to show that places along the shore with the most shoreline recession corresponded to the shallowest section of the lake and that, in general, shoreline recession followed relative changes in near-shore slope. This is exactly what you would expect if the lake level dropped by a constant amount. Around the same time we were analyzing On- tario, we were also investigating smaller southern lakes which disappeared between flybys in 2007 and 2008/2009. By using the Ontario data to calibrate the RADAR’s absorption through the liquid, we were able to derive average loss rates for both Ontario and the small lakes of approximately one meter per year. These results were very exciting because we were not only able to confirm the presence of an active hydrologic cycle; we were able to see it in action! A discussion of these investigations are presented in Chapters 3 and 4 of this work and were published in a pair of papers submitted to the journals Icarus and JGR Planets in 2010 [Hayes et al., 2011, 2010]. These observations represent the first direct evidence for surface liquid playing an active role in Titan’s hydrologic system.
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Simulation, detection, and classification of vessels in maritime SAR images

Simulation, detection, and classification of vessels in maritime SAR images

The ultimate objective of this study has been to explore the application of the basic single- channel acquisitions from spaceborne SAR sensors to automatic vessel monitoring. The surveillance of human activity in the ocean is crucial to safeguard the marine environment, the sustainability of the industrial sector, and the security of navigation. Nowadays, the robustness of existing cooperative vessel monitoring systems can be increased by combining them with other sources of information such as spaceborne remote sensing systems. Among these, the synthetic aperture radar (SAR) imaging technique stands out due to its monitoring advantages: high-resolution images that allow the identification of man-made objects, image acquisitions at a global scale, and non-dependence on natural illumination and weather conditions over the area of interest. Despite the different configuration and capabilities of current civilian spaceborne SAR sensors, all of them are able to provide basic single-channel images. Thus, using these basic acquisitions, the constellation of all these SAR satellites constitutes a family of sensors that can potentially support the continuous monitoring of vessels in any area of the ocean. The presented research work has shown both the feasibility and the limitations of using vessels’ signatures from single-channel SAR images for their detection and feature extraction. The main contributions of this research are:
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A CTFM synthetic aperture sonar

A CTFM synthetic aperture sonar

hydrophone length projector length length of real aperture length of synthetic aperture number of frames per sweep noise power number of integrated pulses isotropic sea noise intensity i[r]

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A New Denoising System for SONAR Images

A New Denoising System for SONAR Images

The SONAR images represent a particular case of Syn- thetic Aperture Radar (SAR) images. The SAR images are perturbed by speckle. It is of multiplicative nature. The aim of a denoising algorithm is to reduce the noise level, while preserving the image features. A first particularity of SONAR images is their potentially low quality. Depending on the acquisition conditions, the Signal to Noise Ratio (SNR) can be very low. A second feature of the SONAR images is that they contain almost homogeneous and textured regions. The presence of edges is relatively rare. The multiplicative speckle noise that disturbs the SONAR images can be transformed into an additive noise with the aid of a logarithm computation block. To obtain the denoising result, the logarithm inversion is performed at the end of the process. A potential architecture for a SONAR denoising system is presented in Figure 1. The denoising system must contain a mean correction block. The corresponding block in Figure 1 computes the mean of the acquired image which is equal with the mean of its noise-free component because the speckle noise has unitary mean. Next it corrects the mean of the result. The mean of the image at the output of the block that inverts the logarithm is extracted and the mean of the acquired image is added.
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Comparative approach of DCT and DWT for SAR Image Fusion

Comparative approach of DCT and DWT for SAR Image Fusion

Abstract— This In this paper presents a novel method based on DCT and DWT image fusion of synthetic aperture radar (SAR) images. Image fusion is the process that combines information from multiple images of the same scene. The result of image fusion is a new image that retains the most desirable information and characteristics of each input image. The proposed approach is applied to generate the fused images by using DCT and DWT. Experiments on real SAR images confirm that the proposed approach using Discrete Wavelet Transform (DWT) does better than the Discrete Cosine Waveform (DCT).
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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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Monostatic Airborne Synthetic Aperture Radar Using Commercial WiMAX Transceivers In the License-exempt Spectrum

Monostatic Airborne Synthetic Aperture Radar Using Commercial WiMAX Transceivers In the License-exempt Spectrum

Synthetic aperture radar (SAR), working in microwave spectrum, plays an increasingly vital role in remote sensing field due to its outstanding feature of forming high-resolution images independent of brightness and weather conditions. This imaging radar requires a moving plat- form such as a satellite, an aircraft or an unmanned aerial system (UAS), to gain the targets’ in- formation of large azimuth bandwidth by collecting numerous returns over a flight path. Since the data acquired during the path produces a high-resolution image by post processing, the rela- tively long path resembles an aerial antenna with large aperture size. For the last three decades, the distinguished clairvoyance of SAR has been extensively utilized for earth surface observa- tion and exploration. Apart from high resolution, this application also demands a large SAR imaging area, which necessitates a spaceborne SAR sensor mounted on an orbiting satellite. Although the earth remote sensing is of great importance for human survival and development, the air surveillance or small-scale remote sensing is indispensable as well in some military and civil applications. For example, airborne SAR is used to the reconnaissance mission for the detection and imaging of intruding vessels day and night on the coastal border. Actually, the thesis was motivated by an interest from industry. A technology company proposed a project to utilize cutting-edge wireless techniques, in order to design a SAR system for carrying out the regional search and rescue for automobiles, aircrafts or ships in distress. Apart from good imaging quality, timeliness is also essential in the above-mentioned examples. To this end, the
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Techniques for Inverse Synthetic Aperture Radar Imaging and monitoring of Deformation with Differential Interferometric Synthetic Aperture Radar

Techniques for Inverse Synthetic Aperture Radar Imaging and monitoring of Deformation with Differential Interferometric Synthetic Aperture Radar

Related to the first issue, an ISAR autofocusing technique based on Doppler parameter estimation has been proposed. The estimation procedure is based on the re-use of Doppler parameter estimation tools available in classical SAR focusing and adapted to perform the ISAR focusing of moving targets. The proposed technique is a parametric methods: it has been assumed that the relative distance between a focusing point taken as reference on the target and the sensor can be approximated by a second order Taylor polynomial; the coefficients of the polynomial function physically represent the radial velocity and the radial acceleration of the target. The proposed method estimates the Doppler signal parameters: the Doppler centroid and the Doppler rate which are mathematically binding to the radial target motion parameters. The ef- fectiveness of the proposed Doppler Parameter Estimation Algorithm (DPEA) has been proven by testing it with a real dataset and by comparing the results with a well-established ISAR autofocus technique: the Image Contrast Based Technique (ICBT), which carries out an exhaustive search aimed at improving a quality parameter: the contrast. The Doppler estimation parameters tool has been adopted firstly for refocusing moving targets in SAR images, and then for focusing moving target sensed by a static ISAR system. From a visual point of view and in terms of image contrast and entropy, the DPEA has shown very good results, which are comparable with those obtained by the ICBT. Fur- thermore, the DPEA seems to produce consistently a higher image peak value and it runs at lower computational cost, making it a strong candidate for a nearly real-time ISAR imaging. It is worth pointing out that the comparison
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Aperture undersampling using compressive sensing for synthetic aperture stripmap imaging

Aperture undersampling using compressive sensing for synthetic aperture stripmap imaging

To the best of our knowledge, a general description on how to apply CS for stripmap synthetic aperture imag- ing is not addressed in the literature yet. In this paper, we use the linear system model of the received echo signals as provided in [26] and link it to the CS framework. We demonstrate based on synthetic data as well as on real data measurements that by regularly undersampling the syn- thetic aperture, CS is capable of successfully suppressing the occurrence of azimuth image ambiguities. This allows that the area coverage rate as a key parameter is improved by increasing the speed of the imaging platform while maintaining the pulse repetition interval. We extend the proposed reconstruction scheme to be used with a single- transmitter and multi-receiver synthetic aperture system as commonly applied in SAS to relax sampling constraints in order to achieve useful coverage rates.
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Bistatic synthetic aperture radar processing

Bistatic synthetic aperture radar processing

SAR usually referred as the imaging radar, utilizes the concept of creating a very long antenna aperture with the help of signal processing techniques. SAR principle was first registered as a patent in 1954 by Carl A. Wiley of the Goodyear Aerospace Corporation, titled “Pulsed Doppler Radar Method and Means” (US Patent Nr. 3.196.436). He used the Doppler information to increase the azimuth resolution of the Side Looking Aper- ture Radar (SLAR). The first satellite carrying a SAR sensor was launched in 1978 by NASA for remote sensing applications. The data collected in a 100 days mission of Seasat-A showed that SAR is a wonderful tool for measuring the Earth’s surface charac- teristics. It is a coherent system which retains both amplitude and phase of the backscat- tered echo signals. It combines the echo signals from each pulse to produce a high reso- lution image of the terrain as compared to that obtained by a much larger antenna. SAR technology can be used with aircrafts (Airborne SAR), satellites (Satel- lite/Spaceborne SAR) or a combination of both aircrafts and satellites (Hybrid SAR). The data collected by SAR sensors is called raw data. Important information lies in the phase of this raw data and with appropriate processing algorithm it can be transformed into a focused SAR image. More details of SAR processing will be provided in the next chapters.
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Synthetic Aperture Ultrasound Image Reconstruction

Synthetic Aperture Ultrasound Image Reconstruction

In this paper, the effect of SA ultrasound imaging was tested to overcome the limitation of conventional ultrasound. The images were reconstructed by using different types of transmission. The results showed that when increasing the number of aperture elements it improved the SNR. The images were reconstructed with and without the FIR Hilbert transform filter. The results were compared with image reconstruction using phase array image reconstruction (raster point technique) and conventional linear array image reconstructed using Ap=1, 2,4,8,16,32, and 64. The SA was shown better results in FOV SNR and lateral resolution. Also SA imaging solved the problem of frame rate and single transmit focusing, but it also has several problems associated with it in terms of penetration depth, flow estimation, and implementation.
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Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years

Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years

Abstract. A new long-term data record of Fram Strait sea ice area export from 1935 to 2014 is developed using a combina- tion of satellite radar images and station observations of sur- face pressure across Fram Strait. This data record shows that the long-term annual mean export is about 880 000 km 2 , rep- resenting 10 % of the sea-ice-covered area inside the basin. The time series has large interannual and multi-decadal vari- ability but no long-term trend. However, during the last decades, the amount of ice exported has increased, with sev- eral years having annual ice exports that exceeded 1 mil- lion km 2 . This increase is a result of faster southward ice drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Green- land. Evaluating the trend onwards from 1979 reveals an in- crease in annual ice export of about + 6 % per decade, with spring and summer showing larger changes in ice export ( + 11 % per decade) compared to autumn and winter ( + 2.6 % per decade). Increased ice export during winter will gener- ally result in new ice growth and contributes to thinning in- side the Arctic Basin. Increased ice export during summer or spring will, in contrast, contribute directly to open water further north and a reduced summer sea ice extent through the ice–albedo feedback. Relatively low spring and summer export from 1950 to 1970 is thus consistent with a higher mid-September sea ice extent for these years. Our results are not sensitive to long-term change in Fram Strait sea ice con-
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Pixel Based Sar Image Classification using Random Forest Algorithm

Pixel Based Sar Image Classification using Random Forest Algorithm

A. Satellite image Analysis and Categorization The analysis of a satellite image into various synthesized regions, called ‘classes’ is one of the complicated issues [6] in the science of Image Analysis. The supervisory categorization methods are highly efficient. But, these methods need the appropriate guidance set, for every pixel of the new image to be classified. Generally, an individual does not have an idea about the various issues that exist in a SAR images. For example, the variation properties of the textures are not known clearly to locate where the textures are present. Similarly, in real-time or other applications, the possibility of deciphering relevant land use/cover information will not be present directly in many images. The major purpose of this paper is to examine some thriving categorization techniques of the day that provide accuracy for analyzing the images of the examined location where sufficient training information is facilitated. These techniques will help us in the effective usage of remote-sensing images [7].
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