Nonparametric Edge Detection in Speckled Imagery

We address the issue of edge detection in Synthetic Aperture Radar imagery. In particular, we propose nonparametric methods for edge detection, and numerically compare them to an alternative method that has been recently proposed in the literature. Our results show that some of the proposed methods display superior results and are computationally simpler than the existing method. An application to real (not simulated) data is presented and discussed.Comment: Accepted for publication in Mathematics and Computers in Simulatio

Research on robust salient object extraction in image

制度:新 ; 文部省報告番号:甲2641号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新480

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Snakes for adapting GIS road and river objects to airborne laser scanning data

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Large-scale 3D environmental modelling and visualisation for flood hazard warning.

3D environment reconstruction has received great interest in recent years in areas such as city planning, virtual tourism and flood hazard warning. With the rapid development of computer technologies, it has become possible and necessary to develop new methodologies and techniques for real time simulation for virtual environments applications. This thesis proposes a novel dynamic simulation scheme for flood hazard warning. The work consists of three main parts: digital terrain modelling; 3D environmental reconstruction and system development; flood simulation models. The digital terrain model is constructed using real world measurement data of GIS, in terms of digital elevation data and satellite image data. An NTSP algorithm is proposed for very large data assessing, terrain modelling and visualisation. A pyramidal data arrangement structure is used for dealing with the requirements of terrain details with different resolutions. The 3D environmental reconstruction system is made up of environmental image segmentation for object identification, a new shape match method and an intelligent reconstruction system. The active contours-based multi-resolution vector-valued framework and the multi-seed region growing method are both used for extracting necessary objects from images. The shape match method is used with a template in the spatial domain for a 3D detailed small scale urban environment reconstruction. The intelligent reconstruction system is designed to recreate the whole model based on specific features of objects for large scale environment reconstruction. This study then proposes a new flood simulation scheme which is an important application of the 3D environmental reconstruction system. Two new flooding models have been developed. The first one is flood spreading model which is useful for large scale flood simulation. It consists of flooding image spatial segmentation, a water level calculation process, a standard gradient descent method for energy minimization, a flood region search and a merge process. The finite volume hydrodynamic model is built from shallow water equations which is useful for urban area flood simulation. The proposed 3D urban environment reconstruction system was tested on our simulation platform. The experiment results indicate that this method is capable of dealing with complicated and high resolution region reconstruction which is useful for many applications. When testing the 3D flood simulation system, the simulation results are very close to the real flood situation, and this method has faster speed and greater accuracy of simulating the inundation area in comparison to the conventional flood simulation model

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Image Processing for Ice Parameter Identification in Ice Management

Various types of remotely sensed data and imaging technology will aid the development of sea-ice observation to, for instance, support estimation of ice forces critical to Dynamic Positioning (DP) operations in Arctic waters. The use of cameras as sensors for offshore operations in ice-covered regions will be explored for measurements of ice statistics and ice properties, as part of a sea-ice monitoring system. This thesis focuses on the algorithms for image processing supporting an ice management system to provide useful ice information to dynamic ice estimators and for decision support. The ice information includes ice concentration, ice types, ice floe position and floe size distribution, and other important factors in the analysis of ice-structure interaction in an ice field. The Otsu thresholding and k-means clustering methods are employed to identify the ice from the water and to calculate ice concentration. Both methods are effective for model-ice images. However, the k-means method is more effective than the Otsu method for the sea-ice images with a large amounts of brash ice and slush. The derivative edge detection and morphology edge detection methods are used to try to find the boundaries of the ice floes. Because of the inability of both methods to separate connected ice floes in the images, the watershed transform and the gradient vector flow (GVF) snake algorithm are applied. In the watershed-based method, the grayscale sea-ice image is first converted into a binary image and the watershed algorithm is carried out to segment the image. A chain code is then used to check the concavities of floe boundaries. The segmented neighboring regions that have no concave corners between them are merged, and over-segmentation lines are removed automatically. This method is applicable to separate the seemingly connected floes whose junctions are invisible or lost in the images. In the GVF snake-based method, the seeds for each ice floe are first obtained by calculating the distance transform of the binarized image. Based on these seeds, the snake contours with proper locations and radii are initialized, and the GVF snakes are then evolved automatically to detect floe boundaries and separate the connected floes. Because some holes and smaller ice pieces may be contained inside larger floes, all the segmented ice floes are arranged in order of increasing size after segmentation. The morphological cleaning is then performed to the arranged ice floes in sequence to enhance their shapes, resulting in individual ice floes identification. This method is applicable to identify non-ridged ice floes, especially in the marginal ice zone and managed ice resulting from offshore operations in sea-ice. For ice engineering, both model-scale and full-scale ice will be discussed. In the model-scale, the ice floes in the model-ice images are modeled as square shapes with predefined side lengths. To adopt the GVF snake-based method for model-ice images, three criteria are proposed to check whether it is necessary to reinitialize the contours and segment a second time based on the size and shape of model-ice floe. In the full-scale, sea-ice images are shown to be more difficult than the model-ice images analyzed. In addition to non-uniform illumination, shadows and impurities, which are common issues in both sea-ice and model-ice image processing, various types of ice (e.g., slush, brash, etc.), irregular floe sizes and shapes, and geometric distortion are challenges in seaice image processing. For sea-ice image processing, the “light ice” and “dark ice” are first obtained by using the Otsu thresholding and k-means clustering methods. Then, the “light ice” and “dark ice” are segmented and enhanced by using the GVF snake-based method. Based on the identification result, different types of sea-ice are distinguished, and the image is divided into four layers: ice floes, brash pieces, slush, and water. This then makes it possible to present a color map of the ice floes and brash pieces based on sizes. It also makes it possible to present the corresponding ice floe size distribution histogram

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Array Manifold Calibration for Multichannel SAR Sounders

This dissertation demonstrates airborne synthetic aperture radar (SAR) sounder array manifold calibration to improve outcomes in two-dimensional and three-dimensional image formation of ice sheet and glacier subsurfaces. The methodology relies on the creation of snapshot databases that aid in both the identification of calibration pixels as well as the validation of proposed calibration strategies. A parametric estimator of nonlinear SAR sounder manifold parameters is derived given a superset of statistically independent and spatially diverse subsets, assuming knowledge of the manifold model. Both measurements-based and computational electromagnetic modeling (CEM) approaches are pursued in obtaining a parametric representation of the manifold that enables the application of this estimator. The former relies on a principal components based characterization of SAR sounder manifolds. By incorporating a subspace clustering technique to identify pixels with a single dominant source, the algorithm circumvents an assumption of single source observations that underlies the formulation of nonparametric methods and traditionally limits the applicability of these techniques to the SAR sounder problem. Three manifolds are estimated and tested against a nominal manifold model in angle estimation and tomography. Measured manifolds on average reduce angle estimation error by a factor of 4.8 and lower vertical elevation uncertainty of SAR sounder derived digital elevation models by a factor of 3.7. Application of the measured manifolds in angle estimation produces 3-D images with more focused scattering signatures and higher intensity pixels that improve automated surface extraction outcomes. Measured manifolds are studied against Method of Moments predictions of the array's response to plane wave excitation obtained with a detailed model of the sounder's array that includes the airborne platform and fairing housing. CEM manifolds reduce angle estimation uncertainty off nadir on average by a factor of 3 when applied to measurements, providing initial confirmation of the utility of the CEM model in predicting angle estimation performance of the sounder's airborne arrays. The research findings of this dissertation indicate that SAR sounder manifold calibration will significantly increase the scientific value of legacy ice sheet and glacier sounding data sets and lead to optimized designs of future remote sensing instrumentation for surveying the cryosphere