Bayesian methods for image fusion

Beyerer, Jürgen; Sander, Jennifer; Heizmann, Michael; Gheta, Ioana

doi:10.1016/b978-0-12-372529-5.00003-2

Cited by 13 publications

(22 citation statements)

References 27 publications

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“…According to these concepts and approaches, the IQ Layer of the high level ISR Analytics Architecture can get designed suitably. Within these contexts, the usefulness of the Bayesian methodology (see, e.g., [7], [8]) for the representation of facts and corresponding uncertainties, for uncertainty based inference, and especially for data and information fusion is demonstrated. Before concluding with Sec.…”

Section: Introductionmentioning

confidence: 99%

ISR analytics: Architectural and methodic concepts

Sander

Schneider

Essendorfer

et al. 2012

2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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Prevention and management of damage scenarios require adequate situation awareness to make timely, coordinated, and proactive decisions possible. The stakeholders must be able to access and to comprehend relevant information quickly and with justifiable effort. The resulting challenges for intelligence, surveillance, and reconnaissance (ISR) lie not only in the new and further development of individual sensor and exploitation systems but also in interoperable system networking as well as in the realization of adequate strategies for the collection, processing, dissemination, and presentation of data and information products [1], [2], [3], [4]. In this publication, we present a high level architecture for ISR analytics that complies with these observations. It provides the functionality to customize the system precisely to specific scenarios of the ISR domain. We give a more detailed insight into concepts and approaches that are essential for specific architecture components

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Section: Introductionmentioning

confidence: 99%

ISR analytics: Architectural and methodic concepts

Sander

Schneider

Essendorfer

et al. 2012

2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)

Self Cite

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“…According to Hadamard (1902), cited via Beyerer et al(2011) a problem is considered to be ill-posed in the following cases: if a solution is not unique; the problem does not have solution; or, the result can become significantly different following a small change in the input data.…”

Section: Dsm Formation and Merging Modelmentioning

confidence: 99%

“…For that reason the DSM resulting from the fusion does not consider natural characteristics, such as smoothness, or other representations of the natural ground (Kotwal and Chaudhuri, 2013). So, in order to overcome the spatial correlation problem, it was necessary to introduce an a priori value which satisfactorily transformed the Maximum Likelihood value into a maximum a posteriori value, and transformed the problem from an ill-posed one into a wellposed one, by introducing an a priori value into the solution of equation 2, which leads to the merged digital surface model (Beyerer et al, 2011). In this section the Bayesian approach is used to invert the forward model equation 1; this model is used to express the digital surface model formation, blended with uncertainty, while incorporating a priori knowledge about the digital surface model (i.e.…”

Section: Merging Using Bayesian Approachmentioning

confidence: 99%

Merging Digital Surface Models Implementing Bayesian Approaches

Sadeq¹,

Drummond²,

Li³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.

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“…The most applications can be found in the ield of image coregistration and image fusion [24][25][26]. In the ield of object detection and delineation reported applications are still rare [27][28][29][30][31]-even more in the ield of remote sensing image analysis [32][33][34][35].…”

Section: Agent-based Image Analysismentioning

confidence: 99%

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Hofmann¹

2017

Modern Fuzzy Control Systems and Its Applications

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Recent methods of image analysis in remote sensing lack a suicient grade of robustness and transferability. Methods such as object-based image analysis (OBIA) achieve satisfying results on single images. However, the underlying rule sets for OBIA are usually too complex to be directly applied on a variety of image data without any adaptations or human interactions. Thus, recent research projects investigate the potential for integrating the agent-based paradigm with OBIA. Agent-based systems are highly adaptive and therefore robust, even under varying environmental conditions. In the context of image analysis, this means that even if the image data to be analyzed varies slightly (e.g., due to seasonal efects, diferent locations, atmospheric conditions, or even a slightly diferent sensor), agent-based methods allow to autonomously adapt existing analysis rules or segmentation results according to changing imaging situations. The basis for individual software agents' behavior is a so-called believe-desire-intention (BDI) model. Basically, the BDI describes for each individual agent its goal(s), its assumed current situation, and some action rules potentially supporting each agent to achieve its goals. The chapter introduces a believe-desire-intention (BDI) model based on fuzzy rules in the context of agent-based image analysis, which extends the classic OBIA paradigm by the agent-based paradigm.Keywords: agent-based image analysis, fuzzy believe-desire-intention model, object-based image analysis, fuzzy control system, remote sensing IntroductionAnalyzing remote sensing data is strongly bound to methods of image processing and image analysis. In contrast to other imaging techniques, remote sensing as per deinition is a method to acquire information about the earth's surface by detecting and analyzing its relected or emited electromagnetic radiation and without being in direct contact with it. Besides radiation © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.of the visual spectrum also infrared (optical data) and microwave radiation (RADAR) is used to produce remote sensing images. The remote sensing instruments can be carried by space crafts (usually satellites) or airborne vehicles (airplanes, drones, etc.). In order to gather geo-information from remote sensing data, the produced images need to be analyzed, that is, preprocessed and classiied. In this context, image classiication means to assign pixels to meaningful object classes of the earth's surface, whereas the delineated and classiied objects are inally stored in a geographic information system (GIS) as polygons, lines, or points ( vector model). With the continuous increase of remote sensing images' spatial (and radiometric) resolution, image analysis in remote sensing became more and more complex. Until...

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Bayesian methods for image fusion

Cited by 13 publications

References 27 publications

ISR analytics: Architectural and methodic concepts

ISR analytics: Architectural and methodic concepts

Merging Digital Surface Models Implementing Bayesian Approaches

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

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