The Advanced Intelligence Decision Support System for the Assessment of Mine-suspected Areas

Several research and development projects have been created to utilize airborne and spaceborne remote sensing for mine action, but the Advanced Intelligence Decision Support System is the first mine-action technology to successfully combine remote sensing with advanced intelligence methodology. The result is a rigorously operationally validated system that improves hazardous risk assessment for greater efficiency in land cancellation and release. This article discusses the components of the AI DSS system and its achievements in mine action

An overview of concepts in fusion of Earth data

International audienceA definition of the data fusion is proposed, which allows to set up a conceptual approach to the fusion of Earth observation data by putting an emphasis on the framework and on the fundamentals in remote sensing underlying data fusion. Further definitions are given which describe the information intervening in any problem of data fusion. Fusion may be performed at different levels: at measurements level, at attribute level, and at rule or decision level. Several problems are to be solved prior to any process of fusion. They deal with either the selection of the representation space and the level of fusion, or with the processing to be applied onto the data. A formalism is discussed which sketches a fusion process. Several examples of fusion processes are given using this formalism

A conceptual approach to the fusion of earth observation data

International audienceData fusion is a subject becoming increasingly relevant as scientists try to extract more and more information from remotely sensed data using their synergy. A definition of data fusion is proposed, which allows to set up a conceptual approach to the fusion of Earth observation data by putting an emphasis on the framework and on the fundamentals in remote sensing underlying data fusion instead of on the tools and means themselves, as is done usually. Further definitions are given, which describe the objects intervening in any problem of data fusion. Fusion may be performed at different levels, simultaneously: measurement level (also improperly called pixel level), at attribute level, and at rule, or decision, level. It is shown that any process of fusion should deal with the selection of the representation space, the level of fusion and the processing to be applied onto the sources of information. The various architectures of fusion systems are presented. Their properties are discussed, including aspects in accuracy, time-consuming, operational constraints. From these basic architectures, more complex systems can be built, which are suitable to a given application

Quality-constrained routing in publish/subscribe systems

Routing in publish/subscribe (pub/sub) features a communication model where messages are not given explicit destination addresses, but destinations are determined by matching the subscription declared by subscribers. For a dynamic computing environment with applications that have quality demands, this is not sufficient. Routing decision should, in such environments, not only depend on the subscription predicate, but should also take the quality-constraints of applications and characteristics of network paths into account. We identified three abstraction levels of these quality constraints: functional, middleware and network. The main contribution of the paper is the concept of the integration of these constraints into the pub/sub routing. This is done by extending the syntax of pub/sub system and applying four generic, proposed by us, guidelines. The added values of quality-constrained routing concept are: message delivery satisfying quality demands of applications, improvement of system scalability and more optimise use of the network resources. We discuss the use case that shows the practical value of our concept

Remote Sensing and Data Fusion for Eucalyptus Trees Identification

Satellite remote sensing is supported by the extraction of data/information from satellite images or aircraft, through multispectral images, that allows their remote analysis and classification. Analyzing those images with data fusion tools and techniques, seem a suitable approach for the identification and classification of land cover. This land cover classification is possible because the fusion/merging techniques can aggregate various sources of heterogeneous information to generate value-added products that facilitate features classification and analysis. This work proposes to apply a data fusion algorithm, denoted FIF (Fuzzy Information Fusion), which combines computational intelligence techniques with multicriteria concepts and techniques to automatically distinguish Eucalyptus trees, in satellite images To assess the proposed approach, a Portuguese region, which includes planted Eucalyptus, will be used. This region is chosen because it includes a significant number of eucalyptus, and, currently, it is hard to automatically distinguish them from other types of trees (through satellite images), which turns this study into an interesting experiment of using data fusion techniques to differentiate types of trees. Further, the proposed approach is tested and validated with several fusion/aggregation operators to verify its versatility. Overall, the results of the study demonstrate the potential of this approach for automatic classification of land types.A deteção remota de imagens de satélite é baseada na extração de dados / informações de imagens de satélite ou aeronaves, através de imagens multiespectrais, que permitem a sua análise e classificação. Quando estas imagens são analisadas com ferramentas e técnicas de fusão de dados, torna-se num método muito útil para a identificação e classificação de diferentes tipos de ocupação de solo. Esta classificação é possível porque as técnicas de fusão podem processar várias fontes de informações heterogéneas, procedendo depois à sua agregação, para gerar produtos de valor agregado que facilitam a classificação e análise de diferentes entidades - neste caso a deteção de eucaliptos. Esta dissertação propõe a utilização de um algoritmo, denominado FIF (Fuzzy Information Fusion), que combina técnicas de inteligência computacional com conceitos e técnicas multicritério. Para avaliar o trabalho proposto, será utilizada uma região portuguesa, que inclui uma vasta área de eucaliptos. Esta região foi escolhida porque inclui um número significativo de eucaliptos e, atualmente, é difícil diferenciá-los automaticamente de outros tipos de árvores (através de imagens de satélite), o que torna este estudo numa experiência interessante relativamente ao uso de técnicas de fusão de dados para diferenciar tipos de árvores. Além disso, o trabalho desenvolvido será testado com vários operadores de fusão/agregação para verificar sua versatilidade. No geral, os resultados do estudo demonstram o potencial desta abordagem para a classificação automática de diversos tipos de ocupação de solo (e.g. água, árvores, estradas etc)

Optimizations on semantic environment management: an application for humanoid robot home assistance

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This article introduces some optimization mechanisms focused on environment management, object recognition, and environment interaction. Although the generality of the presented system, this work will be focused on its application on home assistance humanoid robots. For this purpose, a generic environment formalization procedure for semantic scenery description is introduced. As the main contribution of this work, some techniques for a more efficient use of the environment knowledge are proposed. That way, the application of an areabased discrimination mechanism will avoid to process large amounts of data, useless in the current context, improving the object recognition, and characterizing the available interactions in the current area. Finally, the formalized description, and the optimization procedure, will be tested and verified on a specific home scenario using a humanoid robotThis work has been supported by the Spanish Science and Innovation Ministry MICINN under the CICYT project COBAMI: DPI2011-28507-C02-01/02. The responsibility for the content remains with the authors.Munera Sánchez, E.; Posadas-Yagüe, J.; Poza-Lujan, J.; Blanes Noguera, F.; Simó Ten, JE. (2014). Optimizations on semantic environment management: an application for humanoid robot home assistance. En 2014 IEEE-RAS International Conference on Humanoid Robots. IEEE. 720-725. doi:10.1109/HUMANOIDS.2014.7041442S72072

An information fusion framework for context-based accidents prevention

The oil and gas industry is increasingly concerned with achieving and demonstrating good performance with regard occupational health and safety (OHS) issues, through the control of its OHS risks, which is consistent with its core policy and objectives. There are standards to identify and record workplace accidents and incidents to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in an accident. Therefore, events recognition is central to OHS, since the system can selectively start proper prediction services according to the user current situation and past knowledge taken from huge databases. In this sense, a fusion framework that combines data from multiples sources to achieve more specific inferences is needed. In this paper we propose a machine learning algorithm to learn from past anomalous events related to accident events in time and space. It also uses additional knowledge, like the contextual knowledge: user profile, event location and time, etc. Our proposed model provides the big picture about risk analysis for that employee at that place in that moment in a real world environment. Our main contribution lies in building a causality model for accident investigation by means of well-defined spatiotemporal constraints in the offshore oil industry domain.This work was partially funded by CNPq BJT Project 407851/2012–7 and CNPq PVE Project 314017/2013–5

A Multi Views Approach for Remote Sensing Fusion Based on Spectral, Spatial and Temporal Information

The objectives of this chapter are to contribute to the apprehension of image fusion approaches including concepts definition, techniques ethics and results assessment. It is structured in five sections. Following this introduction, a definition of image fusion provides involved fundamental concepts. Respectively, we explain cases in which image fusion might be useful. Most existing techniques and architectures are reviewed and classified in the third section. In fourth section, we focuses heavily on algorithms based on multi-views approach, we compares and analyses the process model and algorithms including advantages, limitations and applicability of each view. The last part of the chapter summarized the benefits and limitations of a multi-view approach image fusion; it gives some recommendations on the effectiveness and the performance of these methods. These recommendations, based on a comprehensive study and meaningful quantitative metrics, evaluate various proposed views by applying them to various environmental applications with different remotely sensed images coming from different sensors. In the concluding section, we fence the chapter with a summary and recommendations for future researches