98 research outputs found

    Decomposition and unsupervised segmentation of dual-polarized polarimetric SAR data using fuzzy entropy and coherency clustering method

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    En aquesta tesi, el teorema de descomposició H-α i l'algorisme de segmentació fuzzy c-means s'apliquen a un conjunt de dades polarimètriques amb polarització dual d'un sistema SAR (Synthetic Aperture Radar) usant MATLAB i s'avalua la precisió de la segmentació. La segmentació es realitza amb l'objectiu de separar els diferents elements del paisatge emprant les característiques pròpies de cada mecanisme de dispersió. Com es veurà, els paràmetres d'entropia i d'alpha resulten molt valuosos per a diferenciar els diversos tipus d'objectius i l'algorisme fuzzy c-means proposat aplicat a l'entropia i a la matriu de coherència obté resultats robustos en el procés de segmentació. Ambdós algorismes s'apliquen sobre un conjunt de dades de Pangkalan Bun, Indonèsia, proporcionat pel satèl·lit radar TerraSAR-X.En esta tesis, el teorema de descomposición H-α y el algoritmo de segmentación fuzzy c-means se aplican a un conjunto de datos polarimétricos con polarización dual de un sistema SAR (Synthetic Aperture Radar) usando MATLAB y se evalúa la precisión de la segmentación. La segmentación separa los diferentes elementos del paisaje usando las características propias de cada mecanismo de dispersión. Como se verá, los parámetros de entropía y de alpha resultan muy valiosos para diferenciar los tipos de objetivos y el algoritmo fuzzy c-means propuesto aplicado a la entropía y a la matriz de coherencia proporciona resultados robustos en la segmentación. Ambos algoritmos se aplican sobre un conjunto de datos de Pangkalan Bun, Indonesia, proporcionado por el satélite TerraSAR-X.In this thesis, H-α decomposition theorem and fuzzy c-means segmentation algorithm are applied to dual-polarized polarimetric SAR (Synthetic Aperture Radar) data using MATLAB and the accuracy of segmentation is evaluated. The segmentation is done with the purpose of separating the different elements of the landscape using the characteristics of the scattering mechanisms. As it will be shown, entropy and alpha decomposition parameters are a valuable key to differentiate between diverse types of targets and the proposed fuzzy c-means algorithm applied to the entropy and coherency matrix provides robust results in the segmentation process. Both algorithms are applied on a dual-polarized SAR dataset of Pangkalan Bun, Indonesia, provided by TerraSAR-X radar Earth observation satellite

    A Comparison of Fixed Threshold CFAR and CNN Ship Detection Methods for S-band NovaSAR Images

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    NovaSAR is a commercial S-band Synthetic Aperture Radar (SAR) small satellite, built and operated by SSTL in the UK. One of its primary mission objectives is to carry out maritime surveillance and monitoring for security and defence applications. An investigation was carried out into comparing and contrasting conventional and new methods to perform automated ship detection in NovaSAR images. The outcome of this investigation could show the potential effectiveness of ship detection using spaceborne S-band SAR for Maritime Domain Awareness (MDA). The conventional approach is to apply a suitable distribution model to characterise sea surface clutter, followed by the implementation of a fixed threshold, Constant False Alarm Rate (CFAR) detection algorithm. In comparison, a RetinaNet-based convolutional neural network (CNN)solution was developed and trained on an open-source C-band dataset in order to determine the validity of applying non-native training data to S-band imagery. The detection performance was then compared with the CFAR technique, finding that for two selected test acquisitions a CNN-based ship detection algorithm was able to outperform a fixed threshold, CFAR-based method in the absence of native training data. CNN ship detection performance was further improved by applying transfer learning to a native S-band NovaSAR image dataset

    Ship detection on open sea and coastal environment

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    Synthetic Aperture Radar (SAR) is a high-resolution ground-mapping technique with the ability to effectively synthesize a large radar antenna by processing the phase of a smaller radar antenna on a moving platform like an airplane or a satellite. SAR images, due to its properties, have been the focus of many applications such as land and sea monitoring, remote sensing, mapping of surfaces, weather forecasting, among many others. Their relevance is increasing on a daily basis, thus it’s crucial to apply the best suitable method or technique to each type of data collected. Several techniques have been published in the literature so far to enhance automatic ship detection using Synthetic Aperture Radar (SAR) images, like multilook imaging techniques, polarization techniques, Constant False Alarm Rate (CFAR) techniques, Amplitude Change Detection (ACD) techniques among many others. Depending on how the information is gathered and processed, each technique presents different performance and results. Nowadays there are several ongoing SAR missions, and the need to improve ship detection, oil-spills or any kind of sea activity is fundamental to preserve and promote navigation safety as well as constant and accurate monitoring of the surroundings, for example, detection of illegal fishing activities, pollution or drug trafficking. The main objective of this MSc dissertation is to study and implement a set of algorithms for automatic ship detection using SAR images from Sentinel-1 due to its characteristics as well as its ease access. The dissertation organization is as follows: Chapter 1 presents a brief introduction to the theme of this dissertation and its aim, as well as its structure; Chapter 2 summarizes a variety of fundamental key points from historical events and developments to the SAR theory, finishing with a summary of some well-known ship detection methods; Chapter 3 presents a basic guideline to choose the best ship detection technique depending on the data type and operational scenario; Chapter 4 focus on the CFAR technique detailing the implemented algorithms. This technique was selected, given the data set available for testing in this work; Chapter 5 presents the results obtained using the implemented algorithms; Chapter 6 presents the conclusions, final remarks and future work

    Design Options For Low Cost, Low Power Microsatellite Based SAR.

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    This research aims at providing a system design that reduces the mass and cost of spaceborne Synthetic Aperture Radar (SAR) missions by a factor of two compared to current (TecSAR - 300 kg, ~ £ 127 M) or planned (NovaSAR-S — 400 kg, ~ £ 50 M) mission. This would enable the cost of a SAR constellation to approach that of the current optical constellation such as Disaster Monitoring Constellation (DMC). This research has identified that the mission cost can be reduced significantly by: focusing on a narrow range of applications (forestry and disasters monitoring); ensuring the final design has a compact stowage volume, which facilitates a shared launch; and building the payload around available platforms, rather than the platform around the payload. The central idea of the research has been to operate the SAR at a low instantaneous power level—a practical proposition for a micro-satellite based SAR. The use of a simple parabolic reflector with a single horn at L-band means that a single, reliable and efficient Solid State Power Amplifier (SSPA) can be used to lower the overall system cost, and to minimise the impact on the spacecraft power system. A detailed analysis of basic pulsed (~ 5 - 10 % duty cycle) and Continuous Wave (CW) SAR (100 % duty cycle) payloads has shown their inability to fit directly into existing microsatellite buses without involving major changes, or employing more than one platform. To circumvent the problems of pulsed and CW techniques, two approaches have been formulated. The first shows that a CW SAR can be implemented in a mono-static way with a single antenna on a single platform. In this technique, the SAR works in an Interrupted CW (ICW) mode, but these interruptions introduce periodic gaps in the raw data. On processing, these gapped data result in artefacts in the reconstructed images. By applying data based statistical estimation techniques to “fill in the gaps” in the simulated raw SAR data, this research has shown the possibility of minimising the effects of these artefacts. However, once the same techniques are applied to the real SAR data (in this case derived from RADARSAT-1), the artefacts are shown to be problematic. Because of this the ICW SAR design technique it is—set aside. The second shows that an extended chirp mode pulsed (ECMP) SAR (~ 20 - 54 % duty cycle) can be designed with a lowered peak power level which enables a single SSPA to feed a parabolic Cassegrain antenna. The detailed analysis shows the feasibility of developing a microsatellite based SAR design at a comparable price to those of optical missions

    H-α decomposition and unsupervised Wishart classification for dual-polarized polarimetric SAR data

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    En aquesta tesi s'ha aplicat el mètode de descomposició H-α juntament amb el classificador Wishart a un conjunt real de dades SAR (Synthetic Aperture Radar) utilitzant l'entorn MATLAB. El conjunt de dades va ser capturat a través del satèl·lit TerraSAR-X i correspon a la regió de Pangkalan Bun, a Indonèsia. L'objectiu de la tesi és analitzar i avaluar la precisió del classificador. S'ha pogut comprovar que les imatges SAR es poden segmentar utilitzant les propietats dels mecanismes de dispersió caracteritzats pels paràmetres d'entropia i alpha. Per tant, s'ha obtingut una bona segmentació de les diferents zones de la imatge a partir dels paràmetres d'entropia i alpha en combinació amb el classificador Wishart.En esta tesis se ha aplicado el método de descomposición H-α junto con el clasificador Wishart a un conjunto real de datos SAR (Synthetic Aperture Radar) utilizando el MATLAB. El conjunto de datos fue capturado a través del satélite TerraSAR-X y corresponde a la región de Pangkalan Bun, en Indonesia. El objetivo de la tesis es analizar y evaluar la precisión del clasificador. Se ha podido comprobar que la imágenes SAR se pueden segmentar utilizando las propiedades de los mecanismos de dispersión caracterizados por los parámetros de entropía y alpha. Por lo tanto, se ha obtenido una buena segmentación de las distintas zonas de la imagen a partir de los parámetros de entropía y alpha en combinación con el clasificador Wishart.In this thesis the H-α decomposition method and the unsupervised Wishart classifier have been applied to a dual-polarized polarimetric Synthetic Aperture Radar (SAR) dataset using MATLAB computing environment. The dataset was captured by TerraSAR-X satellite and corresponds to the region of Pangkalan Bun, Indonesia. The objective is to analyze and evaluate the precision of the classification. It has been proved that the SAR image can be segmented taking advantage of the properties of the scattering mechanism characterized by the entropy and alpha parameters. Hence, a robust segmentation of the different zones of the image has been obtained by means of the entropy and alpha parameters in combination with the Wishart classifier

    Target detection in SAR images based on a level set approach

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    Abstract This paper introduces a new framework for target detection in SAR images. We focus on the task of locating heterogeneous regions using a level set based algorithm. Unlike most of the approaches in image segmentation, we address an algorithm which incorporates speckle statistics instead of empirical parameters and discards speckle filtering. The curve evolves according to speckle statistics, initially propagating with a maximum upward velocity in homogeneous areas. Our approach is validated by a series of tests on synthetic and real SAR images demonstrating that it represents a novel and efficient method for target detection purpose

    URBAN MODELLING PERFORMANCE OF NEXT GENERATION SAR MISSIONS

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    In synthetic aperture radar (SAR) technology, urban mapping and modelling have become possible with revolutionary missions TerraSAR-X (TSX) and Cosmo-SkyMed (CSK) since 2007. These satellites offer 1m spatial resolution in high-resolution spotlight imaging mode and capable for high quality digital surface model (DSM) acquisition for urban areas utilizing interferometric SAR (InSAR) technology. With the advantage of independent generation from seasonal weather conditions, TSX and CSK DSMs are much in demand by scientific users. The performance of SAR DSMs is influenced by the distortions such as layover, foreshortening, shadow and double-bounce depend up on imaging geometry. In this study, the potential of DSMs derived from convenient 1m high-resolution spotlight (HS) InSAR pairs of CSK and TSX is validated by model-to-model absolute and relative accuracy estimations in an urban area. For the verification, an airborne laser scanning (ALS) DSM of the study area was used as the reference model. Results demonstrated that TSX and CSK urban DSMs are compatible in open, built-up and forest land forms with the absolute accuracy of 8–10 m. The relative accuracies based on the coherence of neighbouring pixels are superior to absolute accuracies both for CSK and TSX

    Speckle reduction in SAR imagery

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    Synthetic Aperture Radar (SAR) is a popular tool for airborne and space-borne remote sensing. Inherent to SAR imagery is a type of multiplicative noise known as speckle. There are a number of different approaches which may be taken in order to reduce the amount of speckle noise in SAR imagery. One of the approaches is termed post image formation processing and this is the main concern of this thesis. Background theory relevant to the speckle reduction problem is presented. The physical processes which lead to the formation of speckle are investigated in order to understand the nature of speckle noise. Various statistical properties of speckle noise in different types of SAR images are presented. These include Probability Distribution Functions as well as means and standard deviations. Speckle is considered as a multiplicative noise and a general model is discussed. The last section of this chapter deals with the various approaches to speckle reduction. Chapter three contains a review of the literature pertaining to speckle reduction. Multiple look methods are covered briefly and then the various classes of post image formation processing are reviewed. A number of non-adaptive, adaptive and segmentation-based techniques are reviewed. Other classes of technique which are reviewed include Morphological filtering, Homomorphic processing and Transform domain methods. From this review, insights can be gained as to the advantages and disadvantages of various methods. A number of filtering algorithms which are either promising, or are representative of a class of techniques, are chosen for implementation and analysis
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