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Guerin C.,French Atomic Energy Commission | Binet R.,French Space Agency | Pierrot-Deseilligny M.,National School of Geographic Sciences
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Research in change detection from optical satellite data is widely investigated as a support for visual image analysis. Most of the methods, however, are based on radiometric changes and are suffering from high false alarms rate due to irrelevant radiometric changes. Change detection based on the elevation difference between two dates, therefore, seems a good alternative to identify relevant changes, especially in a context of urban change detection. In the present work, we provide a fully automatic method of change detection based on a digital surface model (DSM) comparison. The processing flow includes the bundle block adjustment of all the available data as a preprocessing step, followed by an improved DSM generation scheme and a differential DSM analysis. The last two steps have been formulated as labeling problems and solved by an optimization method with a spatial regularization constraint. The solution of these labeling problems is computed with a generalized dynamic programming algorithm that is adapted according to the input data and the defined labels. The final DSMs reach a planimetric and altimetric resolution of about 1 m, allowing changes from 20 m2 to be detected. The results show that 33%-75% (respectively about 95%) of all changes (respectively, changes larger than 100m2) are detected, depending on the employed regularization and the area. Moreover, the calculated kappa coefficient of the processing flow reaches up to 0.80, which emphasizes the method accuracy. All the above features lead to a significant gain compared to the classical visual image analysis. © 2014 IEEE. Source


Sadahiro Y.,University of Tokyo | Lay R.,National School of Geographic Sciences | Kobayashi T.,Florida State University
Transactions in GIS | Year: 2013

Development in techniques of spatial data acquisition enables us to easily record the trajectories of moving objects. Movement of human beings, animals, and birds can be captured by GPS loggers. The obtained data are analyzed by visualization, clustering, and classification to detect patterns frequently or rarely found in trajectories. To extract a wider variety of patterns in analysis, this article proposes a new method for analyzing trajectories on a network space. The method first extracts primary routes as subparts of trajectories. The topological relations among primary routes and trajectories are visualized as both a map and a graph-based diagram. They permit us to understand the spatial and topological relations among the primary routes and trajectories at both global and local scales. The graph-based diagram also permits us to classify trajectories. The representativeness of primary routes is evaluated by two numerical measures. The method is applied to the analysis of daily travel behavior of one of the authors. Technical soundness of the method is discussed as well as empirical findings. © 2012 Blackwell Publishing Ltd. Source


Kalantari M.,National School of Geographic Sciences | Hashemi A.,Isfahan University of Technology | Jung F.,Commissariat General Au Developpement Durable | Guedon J.-P.,CNRS Research Institute of Communication and Cybernetics of Nantes
Journal of Mathematical Imaging and Vision | Year: 2011

This paper presents a new method to solve the relative pose between two images, using three pairs of homologous points and the knowledge of the vertical direction. The vertical direction can be determined in two ways: The first requires direct physical measurements such as the ones provided by an IMU (inertial measurement unit). The other uses the automatic extraction of the vanishing point corresponding to the vertical direction in an image. This knowledge of the vertical direction solves two unknowns among the three parameters of the relative rotation, so that only three homologous couples of points are requested to position a couple of images. Rewriting the coplanarity equations thus leads to a much simpler solution. The remaining unknowns resolution is performed by "hiding a variable" approach. The elements necessary to build a specific algebraic solver are given in this paper, allowing for a real-time implementation. The results on real and synthetic data show the efficiency of this method. © 2010 Springer Science+Business Media, LLC. Source


Rosu A.-M.,CNRS Paris Institute of Global Physics | Pierrot-Deseilligny M.,National School of Geographic Sciences | Delorme A.,CNRS Paris Institute of Global Physics | Binet R.,French National Center for Space Studies | Klinger Y.,CNRS Paris Institute of Global Physics
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2015

Image correlation is one of the most efficient techniques to determine horizontal ground displacements due to earthquakes, landslides, ice flows or sand dune migrations. Analyzing these deformations allows a better understanding of the causes and mechanisms of the events. By using sub-pixel correlation on before- and after-event ortho-images obtained from high resolution satellite images it is possible to compute the displacement field with high planimetric resolution. In this paper, we focus on measuring the ground displacements due to seismotectonic events. The three sub-pixel correlators used are: COSI-Corr - developed by Caltech, a free, closed-source correlator, dependent on commercial software (ENVI) and widely used by the geoscience community for measuring ground displacement; Medicis - developed by CNES, also a closed-source correlator capable of measuring this type of deformation; and MicMac - developed by IGN, the free open-source correlator we study and tune for measuring fine ground displacements. We measured horizontal ground deformation using these three correlators on SPOT images in three study cases: the 2001 Kokoxili earthquake, the 2005 dyke intrusion in the Afar depression and the 2008 Yutian earthquake. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Source


Toutin T.,Canada Center For Remote Sensing | Wang H.,Canada Center For Remote Sensing | Chomaz P.,National School of Geographic Sciences | Pottier E.,University of Rennes 1
IEEE Transactions on Geoscience and Remote Sensing | Year: 2013

Orthorectification using digital terrain models is a key issue for full-polarimetric complex SAR data because resampling the complex data can corrupt the polarimetric phase, mainly in terrain with relief. This research thus compared two methods for the orthorectification of the complex SAR data: The polarimetric processing is performed before (image-space method) or after (ground-space method) the geometric processing. Radarsat-2 fine-quad data acquired with different look angles over a hilly relief study site were orthorectified using accurate light detection and ranging digital surface model. Quantitative evaluations between the two methods as a function of different geometric and radiometric parameters were thus performed to evaluate the impact during orthorectification. The results demonstrated that the look angles and the terrain slopes can potentially corrupt the polarimetric complex SAR data during its orthorectification with the ground-space method. In addition, general advice is provided to reduce these impacts to an acceptable level for the users and their polarimetric applications. © 1980-2012 IEEE. Source

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