Road Network Extraction from Remote Sensing using Region-based Mathematical Morphology

International audienceIn this paper, we introduce an efficient and automatic method for road extraction from satellite or aerial images. It builds upon an existing work based on (incomplete) path opening/closing, morphological filters able to deal with curvilinear structures. We propose here to apply such techniques not on pixels directly but rather on regions representing road segments, to improve both efficiency and robustness. To do so, we map road segments by rectangular areas, which are identified in a first step. We then adapt the morphological filters to process regions instead of pixels. The resulting path filter allows for connection of road segments, in order to identify roads of minimal length. Robustness to occlusion is ensured through the adaptation of the incomplete strategy to our context, while better discrimination between road segments and other objects relies on background knowledge through an hit-or-miss transform. Preliminary results obtained on several satellite images are promising

Human detection from aerial imagery for automatic counting of shellfish gatherers

International audienceAutomatic human identification from aerial image time series or video sequences is a challenging issue. We propose here a complete processing chain that operates in the context of recreational shellfish gatherers counting in a coastal environment (the Gulf of Morbihan, South Brittany, France). It starts from a series of aerial photographs and builds a mosaic in order to prevent multiple occurrences of the same objects on the overlapping parts of aerial images. To do so, several stitching techniques are reviewed and discussed in the context of large aerial scenes. Then people detection is addressed through a sliding window analysis combining the HOG descriptor and a supervised classifier. Several classification methods are compared, including SVM, Random Forests, and AdaBoost. Experimental results show the interest of the proposed approach, and provides directions for future research

Multimodal Object Detection in Remote Sensing

Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area focus on the use of generic object detection and do not leverage the potential of multimodal data fusion. In this paper, we present a comparison of methods for multimodal object detection in remote sensing, survey available multimodal datasets suitable for evaluation, and discuss future directions.Comment: 4 pages, accepted to IGARSS 202

Communicating Active Components: An Environment For Concurrent Applications On Parallel Machines

. Programming the multicomputers is often a delicate job. This explains our interest in the design and the implementation of an Object-Oriented Parallel Language for a multicomputer. This work is part of the VCP (Virtual Class Processor) project. A full Object-Oriented Environment for parallel machines is a long-term goal of this project. This paper presents a unique structuring entity for the multicomputers programming: we call it the Cac (Communicating Active Component). A Cac is an entity which includes an activity which runs a behavioral function, a local context and a mailbox. The behavioral functions and the associated created Cac/s are partitioned in modules which gather the code of application and the subset of Cac/s. The modules are located on the nodes of target machine and a module can be duplicated onto many nodes. The module programming and the modules' duplication are two tools of distribution. The distribution of behavioral functions in the modules can be relayed just b..

Morphological Path Filtering at the Region Scale for Efficient and Robust Road Network Extraction from Satellite Imagery

International audienceRoads are important elements in geographic information systems and remote sensing applications. Their automatic extraction is challenging when only aerial or satellite images are used. Recently, some promising attempts have been made with (incomplete) path opening/closing, morphological filters able to deal with curvilinear structures. We propose here to apply morphological path filters not on pixels directly but rather on regions representing road segments, in order to improve both efficiency and robustness. The overall process is organized in two steps: first we map road segments by rectangular areas made of similar content, before we connect such segments into paths of segments or polylines using region-based path filtering. Robustness to occlusion is ensured through the adaptation of the incomplete path filtering strategy to the region scale, while better discrimination between road segments and other objects is achieved through an hit-or-miss transform that exploits background knowledge. Experiments conducted on several satellite images illustrate the interest of the proposed approach, and shows it outperforms pixelwise detection

Reliability assessment with amalgamated data via the EM algorithm

International audienc

FIABILITIS: A Software for Statistical Analysis of Reliability Data

International audienc

Resource Management for Parallel Adaptive Components

This paper reports the development of the Concerto platform, which is dedicated to supporting the deployment of parallel adaptive components on clusters of workstations. The current work aims at proposing a basic model of a parallel component, together with mechanisms and tools for managing the deployment of such a component. Another objective of this work is to define and implement a scheme that makes it possible for components to perceive their runtime environment. This environment is modelled as a set of resources. Any component can discover and monitor resources, using the services offered by the platform

A Java Middleware Platform for Resource-Aware Distributed Applications

This paper reports the development of D-RAJE (Distributed Resource-Aware Java Environment), a Java-based middleware platform that makes it possible to model and to monitor resources in a distributed environment. With this middleware, any kind of hardware or software resource can be modelled using standard Java objects, and services allow to discover local as well as remote resources, and to observe the state of these resources either locally or remotely. D-RAJE is meant to ease the development of adaptive, security-oriented, or QoS-oriented Java applications, as well as the development of platforms capable of supporting such demanding applications

Middleware Support for the Deployment of Resource-Aware Parallel Java Components on Heterogeneous Distributed Platforms

This paper reports the development of the Concerto platform, which is dedicated to supporting the deployment of resource-aware parallel Java components on heterogeneous distributed platforms, such as pools of workstations in labs or offices. Our work aims at proposing a basic model of a parallel Java component, together with mechanisms and tools for managing the deployment of such a component on a distributed platform. Moreover, we strive to provide components with means to perceive their runtime environment, so they can for example dynamically adapt themselves to changes occurring in this environment. The Concerto platform was designed in order to allow the deployment of parallel components on a distributed platform. It additionally defines and implements an open and extensible framework for distributed resource discovery and monitoring in such an execution environment