177 research outputs found

    Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

    Get PDF
    Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time

    Using redundant information from multiple aerial images for the detection of bomb craters based on marked point processes

    Get PDF
    Many countries were the target of air strikes during World War II. Numerous unexploded bombs still exist in the ground. These duds can be tracked down with the help of bomb craters, indicating areas where unexploded bombs may be located. Such areas are documented in so-called impact maps based on detected bomb craters. In this paper, a stochastic approach based on marked point processes (MPPs) for the automatic detection of bomb craters in aerial images taken during World War II is presented. As most areas are covered by multiple images, the influence of redundant image information on the object detection result is investigated: We compare the results generated based on single images with those obtained by our new approach that combines the individual detection results of multiple images covering the same location. The object model for the bomb craters is represented by circles. Our MPP approach determines the most likely configuration of objects within the scene. The goal is reached by minimizing an energy function that describes the conformity with a predefined model by Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results show a significant improvement with respect to its quality when redundant image information is used. © 2020 Copernicus GmbH. All rights reserved

    Marked point processes for the automatic detection of bomb craters in aerial wartime images

    Get PDF
    Many countries were the target of air strikes during the Second World War. The aftermath of such attacks is felt until today, as numerous unexploded bombs or duds still exist in the ground. Typically, such areas are documented in so-called impact maps, which are based on detected bomb craters. This paper proposes a stochastic approach to automatically detect bomb craters in aerial wartime images that were taken during World War II. In this work, one aspect we investigate is the type of object model for the crater: we compare circles with ellipses. The respective models are embedded in the probabilistic framework of marked point processes. By means of stochastic sampling the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function which describes the conformity with a predefined model. High gradient magnitudes along the border of the object are favoured and overlapping objects are penalized. In addition, a term that requires the grey values inside the object to be homogeneous is investigated. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global optimum of the energy function. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results, based on 22 aerial wartime images, show the general potential of the method for the automated detection of bomb craters and the subsequent automatic generation of an impact map. © Authors 2019

    Registration of Multisensor Images through a Conditional Generative Adversarial Network and a Correlation-Type Similarity Measure

    Get PDF
    The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural characteristics of the input data. Information-theoretic measures are often used to favor comparing local intensity distributions in the images. In this paper, a novel method based on the combination of a deep learning architecture and a correlation-type area-based functional is proposed for the registration of a multisensor pair of images, including an optical image and a synthetic aperture radar (SAR) image. The method makes use of a conditional generative adversarial network (cGAN) in order to address image-to-image translation across the optical and SAR data sources. Then, once the optical and SAR data are brought to a common domain, an area-based â„“2 similarity measure is used together with the COBYLA constrained maximization algorithm for registration purposes. While correlation-type functionals are usually ineffective in the application to multisensor registration, exploiting the image-to-image translation capabilities of cGAN architectures allows moving the complexity of the comparison to the domain adaptation step, thus enabling the use of a simple â„“2 similarity measure, favoring high computational efficiency, and opening the possibility to process a large amount of data at runtime. Experiments with multispectral and panchromatic optical data combined with SAR images suggest the effectiveness of this strategy and the capability of the proposed method to achieve more accurate registration as compared to state-of-the-art approaches

    On Martian Surface Exploration: Development of Automated 3D Reconstruction and Super-Resolution Restoration Techniques for Mars Orbital Images

    Get PDF
    Very high spatial resolution imaging and topographic (3D) data play an important role in modern Mars science research and engineering applications. This work describes a set of image processing and machine learning methods to produce the “best possible” high-resolution and high-quality 3D and imaging products from existing Mars orbital imaging datasets. The research work is described in nine chapters of which seven are based on separate published journal papers. These include a) a hybrid photogrammetric processing chain that combines the advantages of different stereo matching algorithms to compute stereo disparity with optimal completeness, fine-scale details, and minimised matching artefacts; b) image and 3D co-registration methods that correct a target image and/or 3D data to a reference image and/or 3D data to achieve robust cross-instrument multi-resolution 3D and image co-alignment; c) a deep learning network and processing chain to estimate pixel-scale surface topography from single-view imagery that outperforms traditional photogrammetric methods in terms of product quality and processing speed; d) a deep learning-based single-image super-resolution restoration (SRR) method to enhance the quality and effective resolution of Mars orbital imagery; e) a subpixel-scale 3D processing system using a combination of photogrammetric 3D reconstruction, SRR, and photoclinometric 3D refinement; and f) an optimised subpixel-scale 3D processing system using coupled deep learning based single-view SRR and deep learning based 3D estimation to derive the best possible (in terms of visual quality, effective resolution, and accuracy) 3D products out of present epoch Mars orbital images. The resultant 3D imaging products from the above listed new developments are qualitatively and quantitatively evaluated either in comparison with products from the official NASA planetary data system (PDS) and/or ESA planetary science archive (PSA) releases, and/or in comparison with products generated with different open-source systems. Examples of the scientific application of these novel 3D imaging products are discussed

    "Seeing Like A Rover": Images In Interaction On The Mars Exploration Rover Mission

    Full text link
    This dissertation analyzes the use of images on the Mars Exploration Rover mission to both conduct scientific investigations of Mars and plan robotic operations on its surface. Drawing upon three years of fieldwork with the Mars Rover team including ethnography, participant observation, and interviews, the dissertation contributes to the literature in Science and Technology Studies by advancing the analytical framework of drawing as: a practical corollary to Wittgenstein and Hanson's concepts of seeing as that allows the analyst to explore the work of producing scientific images that draw natural objects as analytical objects to enable future representations and interactions. Further, images of Mars betray the social organization of the mission team and its commitment to consensus operations. Observing how images of Mars are drawn as trustworthy documents, drawn as a hypothesis or as a record of collective agreement, drawn as a map for the Rover and drawn as a public space, the disertation demonstrates how interactions with and around Mars Rover images support this political orientation, making the Rover's body a body politic

    The Search for Extraterrestrial Intelligence (SETI)

    Get PDF
    A bibliography of reports concerning the Search for Extraterrestrial Intelligence is presented. Cosmic evolution, space communication, and technological advances are discussed along with search strategies and search systems

    2016 GREAT Day Program

    Get PDF
    SUNY Geneseo’s Tenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1010/thumbnail.jp

    Abstracts for the International Conference on Asteroids, Comets, Meteors 1991

    Get PDF
    Topics addressed include: chemical abundances; asteroidal belt evolution; sources of meteors and meteorites; cometary spectroscopy; gas diffusion; mathematical models; cometary nuclei; cratering records; imaging techniques; cometary composition; asteroid classification; radio telescopes and spectroscopy; magnetic fields; cosmogony; IUE observations; orbital distribution of asteroids, comets, and meteors; solar wind effects; computerized simulation; infrared remote sensing; optical properties; and orbital evolution
    • …
    corecore