Advances in Multi-Sensor Data Fusion: Algorithms and Applications

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of “algorithm fusion” methods; (3) Establishment of an automatic quality assessment scheme

Segmentation non supervisée d'images non stationnaires avec champs de Markov évidentiels

- Fréquemment utilisés en traitement statistique d'images, les champs de Markov cachés (CMC) sont des outils puissants qui peuvent fournir des résultats remarquables. Cette qualité est principalement due à l'aptitude du modèle de prendre en compte des dépendances spatiales des variables aléatoires, même lorsqu'elles sont en très grand nombre, pouvant dépasser le milion. Dans un tel modèle le champ caché X est supposé markovien et doit être estimé à partir du champ observé Y . Un tel traitement est possible du fait de la markovianité de X conditionnellement Y . Ce modèle a été ensuite généralisé au champs de Markov couples (CMCouple), où l'on suppose directement la markovianité du couple (X,Y), qui offrent les mêmes possibilités de traitements que les CMC et permettent de mieux modéliser le bruit ce qui permet, en particulier, de mieux prendre en compte l'existence des textures. Par la suite, les CMCouples ont été généralisés aux champs de Markov triplet (CMT), où la loi du couple (X,Y) est une loi marginale d'un champ de Markov triplet T = (X,U,Y), avec un champ auxiliaire U . Par ailleurs, la théorie de l'évidence peut permettre une amélioration des résultats obtenus par des traitements bayésiens dans certaines situations. Le but de cet article est d'aborder le problème de la segmentation non supervisée d'images non stationnaires en utilisant les champs de Markov évidentiels (CME), en exploitant, en particulier, un lien existant entre les CME et les CMT

A Dempster-Shafer Method for Multi-Sensor Fusion

The Dempster-Shafer Theory, a generalization of the Bayesian theory, is based on the idea of belief and as such can handle ignorance. When all of the required information is available, many data fusion methods provide a solid approach. Yet, most do not have a good way of dealing with ignorance. In the absence of information, these methods must then make assumptions about the sensor data. However, the real data may not fit well within the assumed model. Consequently, the results are often unsatisfactory and inconsistent. The Dempster-Shafer Theory is not hindered by incomplete models or by the lack of prior information. Evidence is assigned based solely on what is known, and nothing is assumed. Hence, it can provide a fast and accurate means for multi-sensor fusion with ignorance. In this research, we apply the Dempster-Shafer Theory in target tracking and in gait analysis. We also discuss the Dempster-Shafer framework for fusing data from a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) sensor unit for precise local navigation. Within this application, we present solutions where GPS outages occur

Detection and height estimation of buildings from SAR and optical images using conditional random fields

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Remote sensing methods for biodiversity monitoring with emphasis on vegetation height estimation and habitat classification

Biodiversity is a principal factor for ecosystem stability and functioning, and the need for its protection has been identified as imperative globally. Remote sensing can contribute to timely and accurate monitoring of various elements related to biodiversity, but knowledge gap with user communities hinders its widespread operational use. This study advances biodiversity monitoring through earth observation data by initially identifying, reviewing, and proposing state-of-the-art remote sensing methods which can be used for the extraction of a number of widely adopted indicators of global biodiversity assessment. Then, a cost and resource effective approach is proposed for vegetation height estimation, using satellite imagery from very high resolution passive sensors. A number of texture features are extracted, based on local variance, entropy, and local binary patterns, and processed through several data processing, dimensionality reduction, and classification techniques. The approach manages to discriminate six vegetation height categories, useful for ecological studies, with accuracies over 90%. Thus, it offers an effective approach for landscape analysis, and habitat and land use monitoring, extending previous approaches as far as the range of height and vegetation species, synergies of multi-date imagery, data processing, and resource economy are regarded. Finally, two approaches are introduced to advance the state of the art in habitat classification using remote sensing data and pre-existing land cover information. The first proposes a methodology to express land cover information as numerical features and a supervised classification framework, automating the previous labour- and time-consuming rule-based approach used as reference. The second advances the state of the art incorporating Dempster–Shafer evidential theory and fuzzy sets, and proves successful in handling uncertainties from missing data or vague rules and offering wide user defined parameterization potential. Both approaches outperform the reference study in classification accuracy, proving promising for biodiversity monitoring, ecosystem preservation, and sustainability management tasks.Open Acces