Combining multiple resolutions into hierarchical representations for kernel-based image classification

Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with different sensors and are naturally fused together through the hierarchical representation, where coarser levels are built from a Low Spatial Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels are generated from a High Spatial Resolution (HSR) or Very High Spatial Resolution (VHSR) image. Such a representation allows one to benefit from the context information thanks to the coarser levels, and subregions spatial arrangement information thanks to the finer levels. Two dedicated structured kernels are then used to perform machine learning directly on the constructed hierarchical representation. This strategy overcomes the limits of conventional GEOBIA classification procedures that can handle only one or very few pre-selected scales. Experiments run on an urban classification task show that the proposed approach can highly improve the classification accuracy w.r.t. conventional approaches working on a single scale.Comment: International Conference on Geographic Object-Based Image Analysis (GEOBIA 2016), University of Twente in Enschede, The Netherland

Cartographie et caractérisation quantitative des paysages de « vallées ordinaires » : L'exemple de la Seulles (Calvados, France)

Les paysages de vallées font l'objet d'une patrimonialisation croissante qui les place au coeur des questions de gestion. Un diagnostic de l'état actuel de ces paysages est nécessaire à l'échelle régionale. Une approche morphologique a révèlé l'importance de ces espaces dans la structuration du paysage régional et l'existence de « vallées ordinaires » aux formes banales. Afin de distinguer ces vallées, nous proposons une démarche reproductible de caractérisation quantitative du paysage pour mesurer la diversité interne à l'échelle du tronçon de vallée (fond de vallée et versants) et rendre compte de sa singularité par rapport au milieu dans lequel il s'insère. A partir d'une vallée représentative et bien renseignée empiriquement, des descripteurs paysagers sont extraits de la cartographie des couvertures du sol réalisée par traitement d'image pour traduire objectivement et quantitativement les caractéristiques du paysage

COASTLINE EXTRACTION IN VHR IMAGERY USING MATHEMATICAL MORPHOLOGY WITH SPATIAL AND SPECTRAL KNOWLEDGE

In this article, we are dealing with the problem of coastline extraction in Very High Resolution (VHR) multispectral images (Quickbird) on the Normandy Coast (France). Locating precisely the coastline is a crucial task in the context of coastal resource management and planning. In VHR imagery, some details on coastal zone become visible and the coastline definition depends on the geomorphologic context. According to the type of coastal units (sandy beach, wetlands, dune, cliff), several definitions for the coastline has to be used. So in this paper we propose a new approach in two steps based on morphological tools to extract coastline according to their context. More precisely, we first perform two detections of possible coastline pixels (respectively without false positive and without false negative). To do so, we apply a recent extension to multivariate images of the hit-or-miss transform, the morphological template matching tool, and rely on expert knowledge to define the sought templates. We then combine these two results through a double thresholding procedure followed by a final marker-based watershed to locate the exact coastline. In order to assess the performance and reliability of our method, results are compared with some ground-truth given by expert visual analysis. This comparison is made both visually and quantitatively. Results show the high performance of our method and its relevance to the problem under consideration

Analyse spatiale et typologie des petites vallées bas-normandes

International audienceAlthough the landscapes of the valleys are original and subject to increasing stakes, they still are not studied very much, because landscape studies are often done to encompassing territorial scales; the studies disregard the specificity of those landscapes. This analysis is based upon the setting up of a typology of landscape of the valleys of the northwest France and the working-out of prospective patterns. Its aim is to help define the policies of "integrated management" which are adapted to those territories. This reasoning is carried out via the realisation of a diagnosis of present landscapes of small valleys following a reasoning which can be reproduced. At first we set up a morphological typology of valleys at a regional scale. After the limits of the valleys have been made explicit (the slopes and the bottoms of the valleys), homogeneous sections are made out. This approach shows the importance of these areas in the structure to the regional landscape and it puts forward new thoughts about the layout of their land use.Bien qu'originaux et soumis à des enjeux croissants, les paysages de vallées demeurent peu étudiés en tant que tels car les études paysagères sont souvent situées à des échelles territoriales englobantes qui négligent leur spécificité. Appuyée sur la mise en place d'une typologie des paysages de vallées représentatifs du Nord Ouest de la France et la construction de scénarii prospectifs, cette réflexion vise à contribuer à la définition de politiques de gestion intégrée adaptées à ces territoires. Cette démarche est menée à travers la réalisation d'un diagnostic de l'état actuel des paysages des petites vallées selon une démarche reproductible Il permet de mettre en place une typologie morphologique des vallées à l'échelle du territoire régional. Après avoir extrait les limites de vallées (versants et fond de vallée) des tronçons homogènes sont distingués. Cette approche révèle l'importance de ces espaces dans la structuration du paysage régional et suggère de nouvelles réflexions sur l'organisation de leurs modes d'occupation du sol

Automated mapping of coastline from high resolution satellite images using supervised segmentation

International audienceIn this article, we are dealing with the problem of coastline extraction in High and Very High Resolution multispectral images. Locating precisely the coastline is a crucial task in the context of coastal resource management and planning. According to the type of coastal units (sandy beach, wetlands, dune, cliff), several definitions for the coastline has to be used. In this paper a new image segmentation method, which is not fully automated but relies on a low intervention of the expert to drive the segmentation process, is proposed. The method combines both a marker-based watershed transform (a standard image segmentation method) and a supervised pixel classification. The user inputs only consist of some spatial and spectral samples which are defined depending on the coastal environment to be monitored. The applicability of the method is tested on various types of coastal environments in France

Modélisation spatiale de la susceptibilité des versants aux mouvements de terrain

International audienceGIS are more and more used to model slopes landslides susceptibility. Among the different methods generally used, bivariate analysis are considered as the more efficient and robust techniques, particularly the Weight of Evidence technique. However, one major drawback of this technique is the problem of information redundancy between predictive variables. It needs an adapted strategy to reduce this conditional dependence problem. This paper presents a strategy to obtain good landslides susceptibility zonation without statistical problems at the 1/10 000 scale in a complex mountainous environment.Les SIG sont de plus en plus utilisés pour modéliser la susceptibilité des versants aux mouvements de terrain. Parmi les différentes méthodes utilisées, les techniques bivariées sont considérées comme les plus robustes et les plus souples à mettre en place, notamment la technique de la théorie de l'évidence. Cependant, l'inconvénient majeur de ce type de technique reste le problème de redondance d'information entre les variables prédictives, ce qui nécessite une stratégie adaptée pour régler le problème de dépendance conditionnelle. A travers une analyse de sensibilité sur les variables à modéliser et sur les variables prédictives, cet article présente une stratégie pour obtenir des cartes de susceptibilité réalistes et statistiquement acceptables pour une échelle du 1 /10 000éme dans un environnement montagneux complexe

Analyse spatiale de la susceptibilité des versants aux glissements de terrain

International audienceLes techniques d'analyse spatialisée par SIG sont de plus en plus utilisées pour évaluer la susceptibilité des versants aux glissements de terrain. Parmi ces méthodes l'analyse bivariée par théorie de l'évidence est considérée comme la plus robuste. Cependant, l'inconvénient principal de cette technique est le problème de redondance d'information entre variables prédictives. L'analyse bivariée par régression logistique, assez peu utilisée actuellement, donne des résultats prometteurs dans ce domaine sans nécessiter un travail d'évaluation préalable des données. Par une analyse de sensibilité sur les variables prédictives, cet article présente une comparaison de ces deux méthodes pour obtenir des cartes de susceptibilité réalistes et statistiquement acceptables pour une échelle du 1 /10 000e dans un environnement montagneux complexe

Landslide susceptibility assessment by bivariate methods at large scales: Application to a complex mountainous environment

International audienceStatistical assessment of landslide susceptibility has become a major topic of research in the last decade. Most progress has been accomplished on producing susceptibility maps at meso-scales (1:50,000–1:25,000). At 1:10,000 scale, which is the scale of production of most regulatory landslide hazard and risk maps in Europe, few tests on the performance of these methods have been performed. This paper presents a procedure to identify the best variables for landslide susceptibility assessment through a bivariate technique (weights of evidence, WOE) and discusses the best way to minimize conditional independence (CI) between the predictive variables. Indeed, violating CI can severely bias the simulated maps by over- or under-estimating landslide probabilities. The proposed strategy includes four steps: (i) identification of the best response variable (RV) to represent landslide events, (ii) identification of the best combination of predictive variables (PVs) and neo-predictive variables (nPVs) to increase the performance of the statistical model, (iii) evaluation of the performance of the simulations by appropriate tests, and (iv) evaluation of the statistical model by expert judgment. The study site is the north-facing hillslope of the Barcelonnette Basin (France), affected by several types of landslides and characterized by a complex morphology. Results indicate that bivariate methods are powerful to assess landslide susceptibility at 1:10,000 scale. However, the method is limited from a geomorphological viewpoint when RVs and PVs are complex or poorly informative. It is demonstrated that expert knowledge has still to be introduced in statistical models to produce reliable landslide susceptibility maps

THE USE OF SENTINEL-2 IMAGERY FOR TOTAL SUSPENDED SOLIDS (TSS) ESTIMATION IN PORONG RIVER, SIDOARJO

ABSTRACT Sidoarjo mud disaster is an occurrence of hot mud bursts at drilling location of Lapindo Brantas Inc., Sidoarjo, Indonesia since 29th May 2006. In order to overcome the continuous mud flow, Indonesian government built embankment around the center of the mudflow. They also throw mud materials into the Porong River. The large and continuous disposal of mud material leads to sedimentation in Porong River. Remote sensing method with satellite imagery can be a solution to find out how much sedimentation occurred in Porong River as a result of mud’s disposal. Total Suspended Solid (TSS) calculation from satellite image can be indicator of sedimentation distribution.In previous studies, TSS distribution has been observed from Landsat-7 and Landsat 8 data. Nowadays, with ability of Sentinel-2 which has higher spatial resolution (10 m) and higher revisit time (up to 6 days), optimization of TSS distribution in Porong river can be done. Thus the objective of this research is analysis of Sentinel-2 imagery application to estimate TSS in Porong River. Result showed good correlation between in-situ data and TSS estimation from Sentinel-2 with value 0.72. Keywords : Sentinel-2, TSS, Porong River

SAMCO: Adaptation de la société aux risques en montagne dans un contexte de changement global

National audienceLe projet SAMCO (Society Adaptation for coping with Mountain risks in a global change COntext, 2013-2016) a pour but de développer un ensemble d'outils méthodologiques permettant d'améliorer la résilience des sociétés soumises aux impacts des risques en milieux montagneux. Ces outils visent à mesu-rer la capacité d'adaptation des écosystèmes et des sociétés à partir de l'analyse de cas réels provenant de 3 sites pilotes représentatifs