National School of Geographic Sciences

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Lisein J.,University of Liège | Lisein J.,National School of Geographic Sciences | Pierrot-Deseilligny M.,National School of Geographic Sciences | Pierrot-Deseilligny M.,Institute National Of Linformation Geeographique Et Forestieere | And 2 more authors.
Forests | Year: 2013

The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and "Structure from Motion" approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series. © 2013 by the authors.


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.


Bhawar R.,University of Basilicata | Di Girolamo P.,University of Basilicata | Summa D.,University of Basilicata | Flamant C.,University Pierre and Marie Curie | And 13 more authors.
Quarterly Journal of the Royal Meteorological Society | Year: 2011

An intensive water vapour intercomparison effort, involving airborne and ground-based water vapour lidar systems, was carried out in the framework of the COPS experiment. The main objective of this paper is to provide accurate error estimates for these systems. Comparisons between the ground-based Raman lidar BASIL and the airborne CNRS DIAL (Differential Absorption Lidar) indicate a mean relative bias between the two sensors, calculated with respect to the mean value of -2.13% (-0.034 g kg-1) in the altitude region 0.5-3.5 km, while comparisons between BASIL and the airborne DLR DIAL lead to a mean relative bias of 1.87% (0.018 g kg-1) in this same altitude region. Comparisons between the ground-based UHOH DIAL and the CNRS DIAL indicate a bias of -3.2% (-0.37 × 1022 m-3) in the altitude range 1.5-4.5 km, while comparisons between the UHOH DIAL and the DLR DIAL indicate a bias of 0.83% (0.06 × 1022 m-3) in this same altitude range. Based on the available comparisons between the ground-based Raman lidar BERTHA and the CNRS DIAL, the mean relative bias is found to be -4.37% (-0.123 g kg-1) in the altitude region 0.5-4.5 km. Comparisons between the ground-based IGN Raman lidar and the CNRS DIAL indicate a bias of 3.18% (0.55 g kg-1) in the altitude range from 0.5 to 4.5 km, while comparisons between the CNRS DIAL and DLR DIAL result in a mean relative bias of 3.93% (1.1 × 1022 m-3) in the altitude interval 0.5-4.0 km. Based on the available statistics of comparisons, benefiting from the fact that the CNRS DIAL was able to be compared with all other lidar systems, and putting equal weight on the data reliability of each instrument, overall relative values for BASIL, BERTHA, IGN Raman lidar, UHOH DIAL, DLR DIAL, and CNRS DIAL, with respect to the mean value, are found to be -0.38, -2.60, 4.90, -1.43, -2.23 and 1.72%, respectively. Copyright © 2011 Royal Meteorological Society Copyright © 2011 Royal Meteorological Society.


Stumpf A.,University of Strasbourg | Malet J.-P.,University of Strasbourg | Allemand P.,CNRS Geological Laboratory of Lyon: earth, planets and environment | Pierrot-Deseilligny M.,National School of Geographic Sciences | Skupinski G.,University of Strasbourg
Geomorphology | Year: 2015

Recent advances in multi-view photogrammetry have resulted in a new class of algorithms and software tools for more automated surface reconstruction. These new techniques have a great potential to provide topographic information for geoscience applications at significantly lower costs than classical topographic and laser scanning surveys. Based on open-source libraries for multi-view stereo-photogrammetry and Structure-from-Motion, this study investigates the accuracy that can be obtained from several processing pipelines for the 3D surface reconstruction of landslides and the detection of changes over time. Two different algorithms for point-cloud comparison are tested and the accuracy of the resulting models is assessed against terrestrial and airborne LiDAR point clouds. Change detection over a period of more than two years allows a detailed assessment of the seasonal dynamics of the landslide; the possibility to estimate sediment volumes and 3D displacement are illustrated for the most active parts of the landslide. Algorithm parameters and the image acquisition protocols are found to have important impacts on the quality of the results and are discussed in detail. © 2014 Elsevier B.V.


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.


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.


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.


Samaan M.,National School of Geographic Sciences | Samaan M.,University of Paris Descartes | Heno R.,National School of Geographic Sciences | Pierrot-Deseilligny M.,National School of Geographic Sciences | Pierrot-Deseilligny M.,University of Paris Descartes
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2013

This article will focus on the first experiments carried out for our PHD thesis, which is meant to make the new image-based methods available for archeologists. As a matter of fact, efforts need to be made to find cheap, efficient and user-friendly procedures for image acquisition, data processing and quality control. Among the numerous tasks that archeologists have to face daily is the 3D recording of very small objects. The Apero/MicMac tools were used for the georeferencing and the dense correlation procedures. Relatively standard workflows lead to depth maps, which can be represented either as 3D point clouds or shaded relief images.


This paper presents a semi-automatic method to optimize supervised object-oriented classification by guided attribute selection without using photo interpretation methods. The studied thematic is the forest (Crecy forest in the north of France). A 20 m spatial resolution SPOT 2 image was analysed in a near infrared color composite. New classification methods class less and less pixels, but regions derived from the previously segmented image. In this context an object oriented method was adopted. Thus the first step consists in image segmentation based on several criteria, a scale parameter and an homogeneity factor made up of two complementary factors: shape and radiometry. Then comes the supervised classification step itself. For all selected training areas, attributes are automatically selected, consecutively based on three criteria: radiometry, shape and texture. Then, three nearest neighbour classifications were computed, each of them with the optimum automatically selected attribute combination derived from the previous step. For each of these classifications, a confusion matrix was computed. For each training area, its confusion rate with other training areas was computed and the lowest confusion rate was selected as right criterion. The classification was assessed by computing the confusion rates, decreasing significantly with respect to a standard nearest neighbour approach, and by editing reliability maps. Test results are so far encouraging. The main originality of this paper is to customize the attributes of the training areas.


De Joinville O.,National School of Geographic Sciences
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010

This paper presents a semi-automatic method to optimize object-oriented classification without photointerpretation. The thematic studied is the forest (Crecy forest in the north of France). A SPOT 2 image at 20 m spatial resolution was analysed in a near infrared colour composite (green, red and infrared). New classification methods no longer work with pixels, but with regions derived from the previously segmented image [TRIAS 2006], [BENCHERIF 2009].The first step consists in image segmentation based on several criteria, a scale parameter and an homogeneity factor made up of two complementary factors: shape and radiometry. 2 segmentations have been computed: one at very large scale (no more than 20 regions) in order to establish a manually made classification with only 2 classes: forest and no forest (this latter will not be classified). Another one at a smaller scale which will be used to select the test samples (also called training area) on the forest area. Once both segmentations and manual classification are completed and validated (essentially visually), the objective of this study is to determine semi automatically the most adapted attributes for each training area (5 training areas have been selected). Therefore, for all selected training areas, attributes are automatically selected, consecutively based on three criteria: radiometry, shape and texture. For each of these criteria, a maximum number of attributes is fixed among all potentially interesting attributes and the optimum attribute combination is automatically selected with respect to a statistical parameter derived from a distance matrix. The distance matrix optimizes the separation between the training areas. Then, 3 classifications were set up, each of them with the optimum automatically selected attribute combination derived from the previous step. For each of these classifications, a confusion matrix will be computed. For each training area its confusion rate with other training areas was computed and the lowest confusion rate was selected as the criterion. For instance, if there is a training area which has 35 % of confusion pixels with other classes for a radiometric combination, 25% for a textural combination and 5 % for a morphologic one (shape criterion), this training area will be affected with a morphologic attribute combination. The result is thus a new classification with the new customized attributes for each training area. In the assessment of this classification, the confusion rate for each class decreases significantly. Then, reliability maps are built to determine the risk of confusion between the classes. Test results are so far encouraging. Due to this new method, the confusion rates decrease significantly with respect to a standard nearest neighbour approach.

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