101 research outputs found

    Mathematical Morphology on the Sphere: Application to Polarimetric Image processing

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    Projecte final de carrera fet en col.laboració amb Centre de morphologie mathématique, École des Mines de ParisEnglish: The fully polarimetric synthetic aperture radar (PolSAR) provides data containing the complete scattering information. Therefore, these data have drawn more attention in recent years. PolSAR data can be represented as polarization states on a sphere. We present image processing techniques based on the analysis of the polarimetric information within its location on the sphere. Mathematical morphology is a well-known nonlinear approach for image processing. It is based on the computation of minimum and maximum values of local neighborhoods. That necessitates the existence of an ordering relationship between the points to be treated. The lack of a natural ordering on the sphere presents an inherent problem when defining morphological operators extended to unit sphere. We analyze in this project some proposals to the problem of ordering on the unit sphere, leading to formulations of morphological operators suited to the configuration of the data. The notion of local supremum and infimum is introduced, which allows to define the dilation and erosion on the sphere. Supervised orderings are considered and its associated operators for target recognition issues. We also present various filtering procedures for denoising purposes. The diferent methods studied in this project pursuit the generalization of the morphological operators on the sphere. Through the analysis performed, we pretend to achieve an understanding of the data and automation of the target detection.Castellano: El radar de apertura sintética totalmente polarimétrico (PolSAR) proporciona datos que contienen la información completa de dispersión. Estos datos han captado más atención en los últimos años. Los datos PolSAR pueden ser representados como estados de polarización en una esfera. Se presentan las técnicas de procesamiento de imágenes basadas en el análisis de la información polarimétrica y en su ubicación en la esfera. La morfología matemática es una técnica no lineal para el procesamiento de imágenes. Se basa en el cálculo de los valores mínimos y máximos alrededor de un punto. Precisa de la existencia de una relación de orden entre los puntos a tratar. La falta de un orden natural en la esfera presenta un problema inherente a la hora de definir los operadores morfológicos extendidos a la esfera unidad. En este proyecto se analizan algunas propuestas para el problema del orden en la esfera unidad, lo que da lugar a formulaciones de los operadores morfológicos adaptados a la configuración de los datos. Se introduce la noción de supremo e ínfimo local, lo que permite definir la dilatación y la erosión en la esfera. Consideramos órdenes supervisados y sus operadores asociados para problemas de reconocimiento de objetivos. También se presentan varios procedimientos de filtrado para la eliminación de ruido. Los diferentes métodos estudiados en este proyecto persiguen la generalización de los operadores morfológicos a la esfera. A través del análisis realizado, se pretende lograr una comprensión de los datos y la autCatalà: El radar d'obertura sintètica totalment polarimètric (PolSAR) proporciona dades que contenen la informació completa de dispersió. Aquestes dades han captat més atenció en els últims anys. Les dades PolSAR poden ser representades com a estats de polarizació en una esfera. Es presenten tècniques de processament d'imatge basades en l'anàlisi de la informació polarimètrica i en la seva ubicació en l'esfera. La morfologia matemàtica és una tècnica no lineal per al processament d'imatges. Es basa en el càlcul dels valors mínim i màxim al voltant d'un punt. Precisa de l'existència d'una relació d'ordre entre els punts a tractar. La manca d'un ordre natural en l'esfera presenta un problema inherent a l'hora de definir els operadors morfològics estesos a l'esfera unitat. En aquest projecte s'analitzen algunes propostes per al problema de l'ordre en l'esfera unitat, el que dóna lloc a formulacions dels operadors morfològics adaptats a la configuració de les dades. S'introdueix la noció de suprem i mínim local, el que permet definir la dilatació i l'erosió en l'esfera. Considerem ordres supervisats i els seus operadors associats per a problemes de reconeixement d'objectius. També es presenten diversos procediments de filtratge per a la eliminació de soroll. Els diferents mètodes estudiats en aquest projecte busquen la generalizació dels operadors morfològics a l'esfera. Mitjançcant l'anàlisi realitzat, es pretén aconseguir la comprensió de les dades i l'a

    Debris Disk Results from the Gemini Planet Imager Exoplanet Survey\u27s Polarimetric Imaging Campaign

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    We report the results of a ∼4 yr direct imaging survey of 104 stars to resolve and characterize circumstellar debris disks in scattered light as part of the Gemini Planet Imager (GPI) Exoplanet Survey. We targeted nearby (≲150 pc), young (≲500 Myr) stars with high infrared (IR) excesses (L IR/L ∗ \u3e 10-5), including 38 with previously resolved disks. Observations were made using the GPI high-contrast integral field spectrograph in H-band (1.6 μm) coronagraphic polarimetry mode to measure both polarized and total intensities. We resolved 26 debris disks and 3 protoplanetary/transitional disks. Seven debris disks were resolved in scattered light for the first time, including newly presented HD 117214 and HD 156623, and we quantified basic morphologies of five of them using radiative transfer models. All of our detected debris disks except HD 156623 have dust-poor inner holes, and their scattered-light radii are generally larger than corresponding radii measured from resolved thermal emission and those inferred from spectral energy distributions. To assess sensitivity, we report contrasts and consider causes of nondetections. Detections were strongly correlated with high IR excess and high inclination, although polarimetry outperformed total intensity angular differential imaging for detecting low-inclination disks (≲70°). Based on postsurvey statistics, we improved upon our presurvey target prioritization metric predicting polarimetric disk detectability. We also examined scattered-light disks in the contexts of gas, far-IR, and millimeter detections. Comparing H-band and ALMA fluxes for two disks revealed tentative evidence for differing grain properties. Finally, we found no preference for debris disks to be detected in scattered light if wide-separation substellar companions were present

    Debris disk results from the Gemini Planet Imager Exoplanet Survey's polarimetric imaging campaign

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    Funding: Supported by NSF grants AST-1411868 (E.L.N., K.B.F., B.M., and J.P.), AST-141378 (G.D.), and AST-1518332 (T.M.E., R.J.D.R., J.R.G., P.K., G.D.). Supported by NASA grants NNX14AJ80G (E.L.N., B.M., F.M., and M.P.), NNX15AC89G and NNX15AD95G/NExSS (T.M.E., B.M., R.J.D.R., G.D., J.J.W, J.R.G., P.K.), NN15AB52l (D.S.), and NNX16AD44G (K.M.M.). M.R. is supported by the NSF Graduate Research Fellowship Program under grant number DGE-1752134. J.R. and R.D. acknowledge support from the Fonds de Recherche du Quèbec. J. Mazoyer’s work was performed in part under contract with the California Institute of Technology/Jet Propulsion Laboratory funded by NASA through the Sagan Fellowship Program executed by the NASA Exoplanet Science Institute. M.M.B. and J.M. were supported by NASA through Hubble Fellowship grants #51378.01-A and HST-HF2-51414.001, respectively, and I.C. through Hubble Fellowship grant HST-HF2-51405.001-A, awarded by the Space Telescope Science Institute, which is operated by AURA, for NASA, under contract NAS5-26555. K.W.D. is supported by an NRAO Student Observing Support Award SOSPA3-007. J.J.W. is supported by the Heising-Simons Foundation 51 Pegasi b postdoctoral fellowship.We report the results of a ∼4 yr direct imaging survey of 104 stars to resolve and characterize circumstellar debris disks in scattered light as part of the Gemini Planet Imager (GPI) Exoplanet Survey. We targeted nearby (≲150 pc), young (≲500 Myr) stars with high infrared (IR) excesses (LIR/L⋆ > 10-5), including 38 with previously resolved disks. Observations were made using the GPI high-contrast integral field spectrograph in H-band (1.6 μm) coronagraphic polarimetry mode to measure both polarized and total intensities. We resolved 26 debris disks and 3 protoplanetary/transitional disks. Seven debris disks were resolved in scattered light for the first time, including newly presented HD 117214 and HD 156623, and we quantified basic morphologies of five of them using radiative transfer models. All of our detected debris disks except HD 156623 have dust-poor inner holes, and their scattered-light radii are generally larger than corresponding radii measured from resolved thermal emission and those inferred from spectral energy distributions. To assess sensitivity, we report contrasts and consider causes of nondetections. Detections were strongly correlated with high IR excess and high inclination, although polarimetry outperformed total intensity angular differential imaging for detecting low-inclination disks (≲70°). Based on postsurvey statistics, we improved upon our presurvey target prioritization metric predicting polarimetric disk detectability. We also examined scattered-light disks in the contexts of gas, far-IR, and millimeter detections. Comparing H-band and ALMA fluxes for two disks revealed tentative evidence for differing grain properties. Finally, we found no preference for debris disks to be detected in scattered light if wide-separation substellar companions were present.Publisher PDFPeer reviewe

    Classification of Compact Polarimetric Synthetic Aperture Radar Images

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    The RADARSAT Constellation Mission (RCM) was launched in June 2019. RCM, in addition to dual-polarization (DP) and fully quad-polarimetric (QP) imaging modes, provides compact polarimetric (CP) mode data. A CP synthetic aperture radar (SAR) is a coherent DP system in which a single circular polarization is transmitted followed by the reception in two orthogonal linear polarizations. A CP SAR fully characterizes the backscattered field using the Stokes parameters, or equivalently, the complex coherence matrix. This is the main advantage of a CP SAR over the traditional (non-coherent) DP SAR. Therefore, designing scene segmentation and classification methods using CP complex coherence matrix data is advocated in this thesis. Scene classification of remotely captured images is an important task in monitoring the Earth's surface. The high-resolution RCM CP SAR data can be used for land cover classification as well as sea-ice mapping. Mapping sea ice formed in ocean bodies is important for ship navigation and climate change modeling. The Canadian Ice Service (CIS) has expert ice analysts who manually generate sea-ice maps of Arctic areas on a daily basis. An automated sea-ice mapping process that can provide detailed yet reliable maps of ice types and water is desirable for CIS. In addition to linear DP SAR data in ScanSAR mode (500km), RCM wide-swath CP data (350km) can also be used in operational sea-ice mapping of the vast expanses in the Arctic areas. The smaller swath coverage of QP SAR data (50km) is the reason why the use of QP SAR data is limited for sea-ice mapping. This thesis involves the design and development of CP classification methods that consist of two steps: an unsupervised segmentation of CP data to identify homogeneous regions (superpixels) and a labeling step where a ground truth label is assigned to each super-pixel. An unsupervised segmentation algorithm is developed based on the existing Iterative Region Growing using Semantics (IRGS) for CP data and is called CP-IRGS. The constituents of feature model and spatial context model energy terms in CP-IRGS are developed based on the statistical properties of CP complex coherence matrix data. The superpixels generated by CP-IRGS are then used in a graph-based labeling method that incorporates the global spatial correlation among super-pixels in CP data. The classifications of sea-ice and land cover types using test scenes indicate that (a) CP scenes provide improved sea-ice classification than the linear DP scenes, (b) CP-IRGS performs more accurate segmentation than that using only CP channel intensity images, and (c) using global spatial information (provided by a graph-based labeling approach) provides an improvement in classification accuracy values over methods that do not exploit global spatial correlation

    Polarimetric Synthetic Aperture Radar

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    This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

    Synthesizing the degree of polarization uniformity from non-polarization-sensitive optical coherence tomography signals using a neural network

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    Degree of polarization uniformity (DOPU) imaging obtained by polarization-sensitive optical coherence tomography (PS-OCT) has the potential to provide biomarkers for retinal diseases. It highlights abnormalities in the retinal pigment epithelium that are not always clear in the OCT intensity images. However, a PS-OCT system is more complicated than conventional OCT. We present a neural-network-based approach to estimate the DOPU from standard OCT images. DOPU images were used to train a neural network to synthesize the DOPU from single-polarization-component OCT intensity images. DOPU images were then synthesized by the neural network, and the clinical findings from ground truth DOPU and synthesized DOPU were compared. There is a good agreement in the findings for RPE abnormalities: recall was 0.869 and precision was 0.920 for 20 cases with retinal diseases. In five cases of healthy volunteers, no abnormalities were found in either the synthesized or ground truth DOPU images. The proposed neural-network-based DOPU synthesis method demonstrates the potential of extending the features of retinal non-PS OCT

    Polarimetric Synthetic Aperture Radar, Principles and Application

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    Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin

    Spatial Modeling of Compact Polarimetric Synthetic Aperture Radar Imagery

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    The RADARSAT Constellation Mission (RCM) utilizes compact polarimetric (CP) mode to provide data with varying resolutions, supporting a wide range of applications including oil spill detection, sea ice mapping, and land cover analysis. However, the complexity and variability of CP data, influenced by factors such as weather conditions and satellite infrastructure, introduce signature ambiguity. This ambiguity poses challenges in accurate object classification, reducing discriminability and increasing uncertainty. To address these challenges, this thesis introduces tailored spatial models in CP SAR imagery through the utilization of machine learning techniques. Firstly, to enhance oil spill monitoring, a novel conditional random field (CRF) is introduced. The CRF model leverages the statistical properties of CP SAR data and exploits similarities in labels and features among neighboring pixels to effectively model spatial interactions. By mitigating the impact of speckle noise and accurately distinguishing oil spill candidates from oil-free water, the CRF model achieves successful results even in scenarios where the availability of labeled samples is limited. This highlights the capability of CRF in handling situations with a scarcity of training data. Secondly, to improve the accuracy of sea ice mapping, a region-based automated classification methodology is developed. This methodology incorporates learned features, spatial context, and statistical properties from various SAR modes, resulting in enhanced classification accuracy and improved algorithmic efficiency. Thirdly, the presence of a high degree of heterogeneity in target distribution presents an additional challenge in land cover mapping tasks, further compounded by signature ambiguity. To address this, a novel transformer model is proposed. The transformer model incorporates both fine- and coarse-grained spatial dependencies between pixels and leverages different levels of features to enhance the accuracy of land cover type detection. The proposed approaches have undergone extensive experimentation in various remote sensing tasks, validating their effectiveness. By introducing tailored spatial models and innovative algorithms, this thesis successfully addresses the inherent complexity and variability of CP data, thereby ensuring the accuracy and reliability of diverse applications in the field of remote sensing

    Recent Advances in Signal Processing

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    The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity
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