A local Gaussian filter and adaptive morphology as tools for completing partially discontinuous curves

This paper presents a method for extraction and analysis of curve--type structures which consist of disconnected components. Such structures are found in electron--microscopy (EM) images of metal nanograins, which are widely used in the field of nanosensor technology. The topography of metal nanograins in compound nanomaterials is crucial to nanosensor characteristics. The method of completing such templates consists of three steps. In the first step, a local Gaussian filter is used with different weights for each neighborhood. In the second step, an adaptive morphology operation is applied to detect the endpoints of curve segments and connect them. In the last step, pruning is employed to extract a curve which optimally fits the template

Using Linear Features for Aerial Image Sequence Mosaiking

With recent advances in sensor technology and digital image processing techniques, automatic image mosaicking has received increased attention in a variety of geospatial applications, ranging from panorama generation and video surveillance to image based rendering. The geometric transformation used to link images in a mosaic is the subject of image orientation, a fundamental photogrammetric task that represents a major research area in digital image analysis. It involves the determination of the parameters that express the location and pose of a camera at the time it captured an image. In aerial applications the typical parameters comprise two translations (along the x and y coordinates) and one rotation (rotation about the z axis). Orientation typically proceeds by extracting from an image control points, i.e. points with known coordinates. Salient points such as road intersections, and building corners are commonly used to perform this task. However, such points may contain minimal information other than their radiometric uniqueness, and, more importantly, in some areas they may be impossible to obtain (e.g. in rural and arid areas). To overcome this problem we introduce an alternative approach that uses linear features such as roads and rivers for image mosaicking. Such features are identified and matched to their counterparts in overlapping imagery. Our matching approach uses critical points (e.g. breakpoints) of linear features and the information conveyed by them (e.g. local curvature values and distance metrics) to match two such features and orient the images in which they are depicted. In this manner we orient overlapping images by comparing breakpoint representations of complete or partial linear features depicted in them. By considering broader feature metrics (instead of single points) in our matching scheme we aim to eliminate the effect of erroneous point matches in image mosaicking. Our approach does not require prior approximate parameters, which are typically an essential requirement for successful convergence of point matching schemes. Furthermore, we show that large rotation variations about the z-axis may be recovered. With the acquired orientation parameters, image sequences are mosaicked. Experiments with synthetic aerial image sequences are included in this thesis to demonstrate the performance of our approach

Road Detection in Dense Urban Areas Using SAR Imagery and the Usefulness of Multiple Views

Abstract—This paper deals with the automatic extraction of the road network in dense urban areas using a few-meters-resolution synthetic aperture radar (SAR) images. The first part presents the proposed method, which is an adaptation of previous work to the specific case of urban areas. The major modifications are 1) the clique potentials of the Markov random field that extracts the road network are adapted and 2) a multiscale framework is used. Re-sults on shuttle mission and aerial SAR images with different res-olutions are presented. The second part is dedicated to road ex-traction combining two SAR images taken with different flight di-rections (orthogonal and antiparallel passes), and the obtained im-provement is analyzed. Index Terms—Different orientation views, Markov random fields, road detection, SAR images. I

Creating Geo-specific Road Databases From Aerial Photos For Driving Simulation

Geo-specific road database development is important to a driving simulation system and a very labor intensive process. Road databases for driving simulation need high resolution and accuracy. Even though commercial software is available on the market, a lot of manual work still has to be done when the road crosssectional profile is not uniform. This research deals with geo-specific road databases development, especially for roads with non-uniform cross sections. In this research, the United States Geographical Survey (USGS) road information is used with aerial photos to accurately extract road boundaries, using image segmentation and data compression techniques. Image segmentation plays an important role in extracting road boundary information. There are numerous methods developed for image segmentation. Six methods have been tried for the purpose of road image segmentation. The major problems with road segmentation are due to the large variety of road appearances and the many linear features in roads. A method that does not require a database of sample images is desired. Furthermore, this method should be able to handle the complexity of road appearances. The proposed method for road segmentation is based on the mean-shift clustering algorithm and it yields a high accuracy. In the phase of building road databases and visual databases based on road segmentation results, the Linde-Buzo-Gray (LBG) vector quantization algorithm is used to identify repeatable cross section profiles. In the phase of texture mapping, five major uniform textures are considered - pavement, white marker, yellow marker, concrete and grass. They are automatically mapped to polygons. In the chapter of results, snapshots of road/visual database are presented

Remote Sensing for International Stability and Security - Integrating GMOSS Achievements in GMES

The Joint Research Centre of the European Commission hosted a two-day workshop "Remote sensing for international stability and security: integrating GMOSS achievements in GMES". Its aim was to disseminate the scientific and technical achievements of the Global Monitoring for Security and Stability (GMOSS) network of excellence to partners of ongoing and future GMES projects such as RESPOND, LIMES, RISK-EOS,PREVIEW, BOSS4GMES, SAFER, G-MOSAIC. The objectives of this workshop were: ¿ To bring together scientific and technical people from the GMOSS NoE and from thematically related GMES projects. ¿ To discuss and compare alternative technical solutions (e.g. final experimental understanding from GMOSS, operational procedures applied in projects such as RESPOND, pre-operational application procedures foreseen from LIMES, etc.) ¿ To draft a list of technical and scientific challenges relevant in the next future. ¿ To open GMOSS to a wider forum in the JRC This report contains abstracts of the fifteen contributions presented by European researchers. The different presentations addressed pre-processing, feature recognition, change detection and applications which represents also the structure of the report. The second part includes poster abstracts presented during a separate poster session.JRC.G.2-Global security and crisis managemen

Fusion d'images optique et radar à haute résolution pour la mise à jour de bases de données cartographiques

Cette thèse se situe dans le cadre de l'interprétation d'images satellite à haute résolution, et concerne plus spécifiquement la mise à jour de bases de données cartographiques grâce à des images optique et radar à haute résolution. Cette étude présente une chaîne de traitement générique pour la création ou la mise à jour de bases de données représentant les routes ou les bâtiments en milieu urbain. En fonction des données disponibles, différents scénarios sont envisagés. Le traitement est effectué en deux étapes. D'abord nous cherchons les objets qui doivent être retirés de la base de données. La seconde étape consiste à rechercher dans les images de nouveaux objets à ajouter dans la base de données. Pour réaliser ces deux étapes, des descripteurs sont construits dans le but de caractériser les objets d'intérêt dans les images d'entrée. L'inclusion ou élimination des objets dans la base de données est basée sur un score obtenu après fusion des descripteurs dans le cadre de la théorie de Dempster-Shafer. Les résultats présentés dans cette thèse illustrent l'intérêt d'une fusion multi-capteurs. De plus l'intégration aisée de nouveaux descripteurs permet à la chaîne d'être améliorable et adaptable à d'autres objets. ABSTRACT : This work takes place in the framework of high resolution remote sensing image analysis. It focuses on the issue of cartographic database creation or updating with optical and SAR images. The goal of this work is to build a generic processing chain to update or create a cartographic database representing roads and buildings in built-up areas. According to available data, various scenarios are foreseen. The proposed processing chain is composed of two steps. First, if a database is available, the presence of each database object is checked in the images. The second step consist of looking for new objects that should be included in the database. To determine if an object should be present in the updated database, relevant features are extracted from images in the neighborhood of the considered object. Those features are based on caracteristics of roads and buildings in SAR and optical images. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of the Dempster-Shafer evidence theory. Results highlight the interest of multi sensor fusion. Moreover the chosen framework allows the easy integration of new features in the processing chai

Extraction des informations sur la morphologie des milieux urbains par analyse des images satellites radars interférométriques

Aujourd’hui, les villes connaissent une croissance exponentielle de leur population. Le suivi de cette croissance est essentiel pour garantir le bien-être des citadins. Cependant, ce suivi nécessite des bases de données cartographiques sur les différents aspects de la morphologie urbaine. Bien que l’interférométrie satellite radar à synthèse d’ouverture (RSO) soit largement exploitée pour la création de modèles numériques de terrain (MNT) et le calcul de la déformation du terrain, son usage en milieu urbain est bien plus complexe, notamment en raison des multiples zones d’inversion, d’occlusion et d’ombre présentes dans ces milieux. Tout d’abord, des algorithmes d’extraction de l’information 2D sur la morphologie urbaine (emprise au sol des bâtiments, occupation du sol et réseau routier), s’appuyant uniquement sur des données satellites RSO mono-polarisées, ont été implémentés. L’accent a été mis sur le caractère robuste, automatique et rapide de ces algorithmes. Les résultats obtenus sont comparables à ceux présentés à partir d’images aéroportées. Après avoir testé les algorithmes à partir des images satellites en amplitude, l’apport des produits interférométriques (interférogramme et cohérence) a été évalué. Il résulte de cette approche en deux étapes que les produits interférométriques, en raison de leur faible résolution, ont un réel impact uniquement sur la segmentation des éléments de tailles importantes. En ce qui concerne l’extraction de l’information 3D sur la hauteur des bâtiments, une procédure s’appuyant sur deux interférogrammes, l’un possédant une petite ligne de base, et l’autre une grande ligne de base, a été développée. L’utilisation de ces deux interférogrammes permet de détecter la majorité des sauts de phase, tout en conservant une précision convenable. Toutefois, cette procédure n’aurait pas été optimale sans l’apport des informations 2D extraites ci-dessus, tant pour le calcul de la hauteur des bâtiments que pour la génération du MNT. L’apport de ces informations a, notamment, permis d’exclure les zones d’inversion, d’occlusion et d’ombre, qui génèrent une valeur aléatoire pour la phase.Nowadays, towns are undergoing exponential growth. The monitoring of their expansion is essential to guarantee the welfare of citizens. To do that, cartographic databases of multiple aspects of urban morphology are required. Satellite imaging using interferometric synthetic aperture radar (SAR) is widely applied to generate digital terrain models (DTM) and calculate ground deformations. However, satellite interferometric SAR in urban zones is much more complex, due in part to numerous reversal, occlusion and shaded areas. First of all, algorithms to extract the 2D information on urban morphology (building footprints, land cover and road network) have been implemented. These algorithms are based only on single-polarized satellite SAR data. The decision on the type of approach was driven by robustness, automatic and speed criteria. Achieved results are comparable to results presented with aircraft images. Once algorithms have been tested on satellite intensity images, the contribution of interferometric products (interferogram and coherence) have been evaluated. Thanks to this two-step approach, we found that interferometric products have a significant contribution to segment big size objects only. Concerning the extraction of the 3D information on building heights, a method based on two interferograms, with a short and a long baseline respectively, has been developed. This approach allows to detect a large number of phase jumps while preserving a reasonable accuracy. However, this method would not have been possible without the contribution of the 2D information extracted earlier, whether for the calculation building height or for the generation of DTM. Among other things, this additional information allows to resolve the phase disturbance generated by reversal, occlusion and shaded areas

Statistical Fusion of Multi-aspect Synthetic Aperture Radar Data for Automatic Road Extraction

In this dissertation, a new statistical fusion for automatic road extraction from SAR images taken from different looking angles (i.e. multi-aspect SAR data) was presented. The main input to the fusion is extracted line features. The fusion is carried out on decision-level and is based on Bayesian network theory