Analyse hiérarchique d'images multimodales

There is a growing interest in the development of adapted processing tools for multimodal images (several images acquired over the same scene with different characteristics). Allowing a more complete description of the scene, multimodal images are of interest in various image processing fields, but their optimal handling and exploitation raise several issues. This thesis extends hierarchical representations, a powerful tool for classical image analysis and processing, to multimodal images in order to better exploit the additional information brought by the multimodality and improve classical image processing techniques. %when applied to real applications. This thesis focuses on three different multimodalities frequently encountered in the remote sensing field. We first investigate the spectral-spatial information of hyperspectral images. Based on an adapted construction and processing of the hierarchical representation, we derive a segmentation which is optimal with respect to the spectral unmixing operation. We then focus on the temporal multimodality and sequences of hyperspectral images. Using the hierarchical representation of the frames in the sequence, we propose a new method to achieve object tracking and apply it to chemical gas plume tracking in thermal infrared hyperspectral video sequences. Finally, we study the sensorial multimodality, being images acquired with different sensors. Relying on the concept of braids of partitions, we propose a novel methodology of image segmentation, based on an energetic minimization framework.Il y a un intérêt grandissant pour le développement d’outils de traitements adaptés aux images multimodales (plusieurs images de la même scène acquises avec différentes caractéristiques). Permettant une représentation plus complète de la scène, ces images multimodales ont de l'intérêt dans plusieurs domaines du traitement d'images, mais les exploiter et les manipuler de manière optimale soulève plusieurs questions. Cette thèse étend les représentations hiérarchiques, outil puissant pour le traitement et l’analyse d’images classiques, aux images multimodales afin de mieux exploiter l’information additionnelle apportée par la multimodalité et améliorer les techniques classiques de traitement d’images. Cette thèse se concentre sur trois différentes multimodalités fréquemment rencontrées dans le domaine de la télédétection. Nous examinons premièrement l’information spectrale-spatiale des images hyperspectrales. Une construction et un traitement adaptés de la représentation hiérarchique nous permettent de produire une carte de segmentation de l'image optimale vis-à-vis de l'opération de démélange spectrale. Nous nous concentrons ensuite sur la multimodalité temporelle, traitant des séquences d’images hyperspectrales. En utilisant les représentations hiérarchiques des différentes images de la séquence, nous proposons une nouvelle méthode pour effectuer du suivi d’objet et l’appliquons au suivi de nuages de gaz chimique dans des séquences d’images hyperspectrales dans le domaine thermique infrarouge. Finalement, nous étudions la multimodalité sensorielle, c’est-à-dire les images acquises par différents capteurs. Nous appuyant sur le concept des tresses de partitions, nous proposons une nouvelle méthodologie de segmentation se basant sur un cadre de minimisation d’énergie

Segmentation hiérarchique d'images multimodales

National audienceHierarchies of partitions are widely used in the context of image segmentation. However,in the case of multimodal images, the fusion of multiple hierarchies remains a challenge. Recently, braids of partitions have been proposed as a possible solution to this issue, but have never been implemented in a practical case.In this paper, we propose a new methodology to achieve multimodal segmentation, based on this notion of braids of partitions. This new method is applied in a practical case, namely the joint segmentation of hyperspectral and LiDAR data. Obtained results confirm the potential of the proposed method.Les hiérarchies de partitions sont couramment utilisées pour segmentation d'images. Dans le cas d'images multimodales toutefois, la fusion de plusieurs hiérarchies reste un problème. Récemment, les tresses de partitions ont été proposées comme une possible solution à ce problème, mais n'ont jamais été implémentées dans un cas pratique. Nous proposons ainsi une nouvelle méthodologie, basée sur cette notion de tresse de partitions, pour effectuer la segmentation d'images multimodales. Cette méthode est appliquée dans un cas concret, à savoir la segmentation conjointe de données hyperspectrales et LiDAR. Les résultats obtenus confirment le potentiel de la méthode proposée

Segmentation of Multimodal Images based on Hierarchies of Partitions

International audienceHierarchies of partitions are widely used in the context of image segmentation, but when it comes to multimodal images, the fusion of multiple hierarchies remains a challenge. Recently, braids of partitions have been proposed as a possible solution to this issue, but have never been implemented in a practical case. In this paper, we propose a new methodology to achieve multimodal segmentation based on this notion of braids of partitions. We apply this new method in a practical example, namely the segmentation of hyperspectral and LiDAR data. Obtained results confirm the potential of the proposed method

Unmixing-based gas plume tracking in LWIR hyperspectral video sequences

International audienceIt is now possible to collect hyperspectral video sequences (HVS) at a near real-time frame rate. The wealth of spectral , spatial and temporal information of those sequences is particularly appealing for chemical gas plume tracking. Existing state-of-the-art methods for such applications however produce only a binary information regarding the position and shape of the gas plume in the HVS. Here, we introduce a novel method relying on spectral unmixing considerations to perform chemical gas plume tracking, which provides information related to the gas plume concentration in addition to its spatial localization. The proposed approach is validated and compared with three state-of-the-art methods on a real HVS

From local to global unmixing of hyperspectral images to reveal spectral variability

International audienceThe linear mixing model is widely assumed when unmixing hyperspectral images, but it cannot account for endmembers spectral variability. Thus, several workarounds have arisen in the hyperspectral unmixing literature, such as the extended linear mixing model (ELMM), which authorizes endmembers to vary pixelwise according to scaling factors, or local spectral unmixing (LSU) where the unmixing process is conducted locally within the image. In the latter case however, results are difficult to interpret at the whole image scale. In this work, we propose to analyze the local results of LSU within the ELMM framework, and show that it not only allows to reconstruct global endmembers and fractional abundances from the local ones, but it also gives access to the scaling factors advocated by the ELMM. Results obtained on a real hyperspectral image confirm the soundness of the proposed methodology

A comparison study between windowing and binary partition trees for hyperspectral image information mining

International audienceRemote sensors capture large scenes that are conventionally split in smaller patches before being stored and analyzed. Traditionally, this has been done by dividing the scene in rectangular windows. Such windowing methodology could provoke the separation of spectrally homogeneous areas or objects of interest into two or more patches. This is due to the presence of objects of interest in correspondence to windows' borders, or because the fixed size of the windows does not adapt well to the scale of the objects. To alleviate this issue, the windows can be arranged in an overlapping way, incurring in some data redundancy storage. Recently, tree representations have been used as an alternative to windowing in order to structure and store large amounts of remote sensing data. In this work we explore the benefits of using Binary Partition Trees (BPT) instead of windowing to store hyperspectral large scenes. We are particularly interested in storing the information resulting of local spectral unmixing processes running over a large real hyperspectral scene. We show that under similar conditions BPT allows a better storage of the unmixing information in terms of reconstruction error

Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation

International audienceThe Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a tree structure. BPT allows users to explore the image at different segmentation scales. Often, the tree is pruned to get a more compact representation and so the remaining nodes conform an optimal partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear Spectral Unmixing (LSU) consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions

Neural Koopman prior for data assimilation

With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data. While neural networks are often seen as uninterpretable black-box architectures, they can still benefit from physical priors on the data and from mathematical knowledge. In this paper, we use a neural network architecture which leverages the long-known Koopman operator theory to embed dynamical systems in latent spaces where their dynamics can be described linearly, enabling a number of appealing features. We introduce methods that enable to train such a model for long-term continuous reconstruction, even in difficult contexts where the data comes in irregularly-sampled time series. The potential for self-supervised learning is also demonstrated, as we show the promising use of trained dynamical models as priors for variational data assimilation techniques, with applications to e.g. time series interpolation and forecasting

Gas Plume Detection and Tracking in Hyperspectral Video Sequences using Binary Partition Trees

International audienceThanks to the fast development of sensors, it is now possible to acquire sequences of hyperspectral images. Those hyperspectral video sequences are particularly suited for the detection and tracking of chemical gas plumes. However, the processing of this new type of video sequences with the additional spectral diversity, is challenging and requires the design of advanced image processing algorithms. In this paper, we present a novel method for the segmentation and tracking of a chemical gas plume diffusing in the atmosphere, recorded in a hyperspectral video sequence. In the proposed framework, the position of the plume is first estimated, using the temporal redundancy of two consecutive frames. Second, a Binary Partition Tree is built and pruned according to the previous estimate, in order to retrieve the real location and extent of the plume in the frame. The proposed method is validated on a real hyperspectral video sequence and compared with a state-of-the-art method

Hyperspectral image segmentation using a new spectral mixture-based binary partition tree representation

International audienceThe Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a tree structure. BPT allows users to explore the image at different segmentation scales, from fine partitions close to the leaves to coarser partitions close to the root. Often, the tree is pruned so the leaves of the resulting pruned tree conform an optimal partition given some optimality criterion. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. The proposed methodology exploits the local unmixing of the regions to find the partition achieving a global minimum reconstruction error. We successfully tested the proposed approach on the well-known Cuprite hyperspectral image collected by NASA Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This scene is considered as a standard benchmark to validate spectral unmixing algorithms