Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics

International audienceIn this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: (1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low level change information between the time layers and object level building description to recognize and separate changed and unaltered buildings. (2) To answering the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. (3) To simultaneously ensure the convergence, optimality and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel non-uniform stochastic object birth process, which generates relevant objects with higher probability based on low-level image features

A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects.

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns

Photometric stereo with only two images: A theoretical study and numerical resolution

This work tackles the problem of two-image photometric stereo. This problem constitutes the intermediate case between conventional photometric stereo with at least three images, which is well-posed, and shape-from-shading, which is ill-posed. We first provide a theoretical study of ambiguities arising in this intermediate case. Based on this study, we show that when the albedo is known, disambiguation can be formulated as a binary labeling problem, using integrability and a nonstationary Ising model. The resulting optimization problem is solved efficiently by resorting to the graph cut algorithm. These theoretical and numerical contributions are eventually validated in an application to three-image photometric stereo with shadows.Roberto Mecca was a Marie Curie Fellow of the Instituto Nazionale di Alta Matematic

Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours.

We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. The approach can be viewed as an extension of the active contour methodology to an a priori unknown number of objects. Sampling and estimation are based on a stochastic birth-and-death process defined in a space of multiple, arbitrarily shaped objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data

Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval.

Retrieval from remote sensing image archives relies on the extraction of pertinent information from the data about the entity of interest (e.g. land cover type), and on the robustness of this extraction to nuisance variables (e.g. illumination). Most image-based characterizations are not invariant to such variables. However, other semantic entities in the image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. Road networks are one example: their properties vary considerably, for example, from urban to rural areas. This paper takes the first steps towards classification (and hence retrieval) based on this idea. We study the dependence of a number of network features on the class of the image ('urban' or 'rural'). The chosen features include measures of the network density, connectedness, and 'curviness'. The feature distributions of the two classes are well separated in feature space, thus providing a basis for retrieval. Classification using kernel k-means confirms this conclusion

Indexing of mid-resolution satellite images with structural attributes.

Satellite image classification has been a major research field for many years with its varied applications in the field of Geography, Geology, Archaeology, Environmental Sciences and Military purposes. Many different techniques have been proposed to classify satellite images with color, shape and texture features. Complex indices like Vegetation index (NDVI), Brightness index (BI) or Urban index (ISU) are used for multi-spectral or hyper-spectral satellite images. In this paper we will show the efficiency of structural features describing man-made objects in mid-resolution satellite images to describe image content. We will then show the state-of-the-art to classify large satellite images with structural features computed from road networks and urban regions extracted on small image patches cut in the large image. Fisher Linear Discriminant (FLD) analysis is used for feature selection and a one-vsrest probabilistic Gaussian kernel Support Vector Machines (SVM) classification method is used to classify the images. The classification probabilities associated with each subimage of the large image provide an estimate of the geographical class coverage

Some improvements to bayesian image segmentation. Part two : Classification

We consider the automatic classification framework, using a Markov model . We address the problem of estimating the number of classes and the associated class parameters . We propose a method using the contextual information inherent in images to discriminate different classes in the case of mixture distributions with strongly mixed classes . This method is validated theoretically and practically, using synthetical images and real data . We prove that the proposed method has a validity domain larger than the methods based on a histogram analysis . We then discuss the shape of the data driven potential induced by the detected classes in a Markovian framework . Results are obtained by using two priors : the Potts model and the Chien-model .Nous nous plaçons dans le cadre de la classification automatique. Nous abordons le problème de l'estimation du nombre de classes et des paramètres qui leurs sont associés. Nous proposons une méthode utilisant l'hypothèse contextuelle inhérente aux images pour discriminer les différentes classes. Cette méthode est validée à la fois sur le plan théorique et sur des images de synthèse et des images réelles. Nous montrons, en outre, que la méthode proposée a un domaine de validité plus étendu que les méthodes fondées sur une analyse des modes de l'histogramme. Nous discutons ensuite de la forme du potentiel d'attache aux données dérivé de cette classification dans le cadre d'une segmentation markovienne. Les résultats sont obtenus avec deux modèles a priori différents : le modèle de Potts et le chien-modèle

Some improvements to bayesian image segmentation. Part one : Modelling

In this paper we address the segmentation problem in a Bayesian framework. Of the three stages (modelling, estimation , optimisation), we consider modelling and optimisation . We consider modelling by Markov random fields . We demonstrate the limitations of the Potts model currently employed, and propose a new model (the chien model) which allows us to control the boundary length and lines in the segmented images . We also preserve fine structures in the data . Then, we compare the MPM and MAP criteria when used with the algorithms discussed above . Results are presented on synthetic images and SPOT data . The classification problem is tackled in a second part .Nous nous plaçons dans le cadre de la segmentation bayésienne. Parmi les trois étapes (modélisation, estimation, optimisation), nous considérons la modélisation et l'optimisation. La modélisation est appréhendée sous l'angle des champs de Markov. Nous montrons les limites du modèle de Potts couramment employé et proposons un nouveau modèle (le chien-modèle) permettant de contrôler la longueur des contours et des lignes dans l'image segmentée. Nous préservons ainsi les structures fines présentes dans les données. Nous comparons ensuite les critères MPM et MAP conjointement aux algorithmes qui permettent de les optimiser. Les différents résultats sont obtenus sur des images synthétiques et des images SPOT. Le problème de la classification fait l'objet d'une seconde partie

Multiple Birth and Cut Algorithm for Point Process Optimization

International audienceIn this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to the energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing the advantages and disadvantages, where the most important advantage is the reduction of the number of parameters. We validated our algorithm on the counting problem of flamingos in colony, where our algorithm outperforms the performance of the MBD algorithm

A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns