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Spectral and spatial algorithm architecture for classification of hyperspectral and LIDAR

Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Rand, Robert S. Khuon, Timothy S. National Geospatial-Intelligence Agency Office of Innovision Basic and Applied Research 7500 GEOINT Drive Springfield VA 22153 United States

ISBN: (纸本)9780819490858

architecture for neural net multi-sensor data fusion is introduced and analyzed. This architecture consists of a set of independent sensor neural nets, one for each sensor, coupled to a fusion net. The neural net of each sensor is trained (from a representative data set of the particular sensor) to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. To begin the processing, the 3D point cloud LIDAR data is classified based on a multi-dimensional mean-shift segmentation and classification into clustered objects. Similarly, the multi-band HSI data is spectrally classified by the Stochastic Expectation-Maximization (SEM) into a classification map containing pixel classes. For sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The first layer is the sensor level consisting of two neural nets: spatial neural net and spectral neural net. The second level consists of a single neural net, that is the fusion neural net. The success of the system in utilizing sensor synergism for an enhanced classification is clearly demonstrated by applying this architecture for classifying on November 2010 airborne data collection of LIDAR and HSI over the Gulfport, MS, area. © 2012 SPIE.

关键词： Sensor data fusion

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Navigation of uncertain terrain by fusion of information from real and synthetic imagery

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Lyons, Damian M. Nirmal, Paramesh Paul Benjamin, D. Fordham University Robotics and Computer Vision Laboratory Bronx NY 10458 United States Pace University Department of Computer Science New York NY 10023 United States

ISBN: (纸本)9780819490858

We consider the scenario where an autonomous platform that is searching or traversing a building may observe unstable masonry or may need to travel over unstable rubble. A purely behaviour-based system may handle these challenges but produce behaviour that works against long-terms goals such as reaching a victim as quickly as possible. We extend our work on ADAPT, a cognitive robotics architecture that incorporates 3D simulation and image fusion, to allow the robot to predict the behaviour of physical phenomena, such as falling masonry, and take actions consonant with long-term goals. We experimentally evaluate a cognitive only and reactive only approach to traversing a building filled with various numbers of challenges and compare their performance. The reactive only approach succeeds only 38% of the time, while the cognitive only approach succeeds 100% of the time. While the cognitive only approach produces very impressive behaviour, our results indicate how much better the combination of cognitive and behaviour-based can be. © 2012 SPIE.

关键词： Robots

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Homeland situation awareness through mining and fusing heterogeneous information from intelligence databases and field sensors

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Digioia, Giusj Panzieri, Stefano Computer and Automation Department Roma Tre University Via della Vasca Navale 79 00146 Rome Italy Engineering Ingegneria Informatica SpA Defense and Security Department Via Riccardo Morandi 32 00148 Ponte Galeria Rome Italy

ISBN: (纸本)9780819490858

One of the most felt issues in the defence domain is that of having huge quantities of data stored in databases and acquired from field sensors, without being able to infer information from them. Usually databases are continuously updated with observations, and are related to heterogeneous data. Deep and continuous analysis on data could mine useful correlations, explain relations existing among data and cue searches for further evidences. The solution to the problem addressed before seems to deal both with the domain of Data Mining and with the domain of high level Data fusion, that is Situation Assessment, Threat Assessment and Process Refinement, also synthesised as Situation Awareness. The focus of this paper is the definition of an architecture for a system adopting data mining techniques to adaptively discover clusters of information and relation among them, to classify observations acquired and to use the model of knowledge and the classification derived in order to assess situations, threats and refine the search for evidences. Sources of information taken into account are those related to the intelligence domain, as IMINT, HUMINT, ELINT, COMINT and other non-conventional sources. The algorithms applied refer to not supervised and supervised classification for rule exploitation, and adaptively built Hidden Markov Model for situation and threat assessment. © 2012 SPIE.

关键词： Data mining

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Advances in data representation for hard/soft information fusion

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Rimland, Jeffrey C. Coughlin, Dan Hall, David L. Graham, Jacob L. Pennsylvania State University College of IST 101IST Building University Park PA 16802-6822 United States Pennsylvania State University OE03 Paterno Library University Park PA 16802 United States

ISBN: (纸本)9780819490858

information fusion is becoming increasingly human-centric. While past systems typically relegated humans to the role of analyzing a finished fusion product, current systems are exploring the role of humans as integral elements in a modular and extensible distributed framework where many tasks can be accomplished by either human or machine performers. For example, "participatory sensing" campaigns give humans the role of "soft sensors" by uploading their direct observations or as "soft sensor platforms" by using mobile devices to record human-annotated, GPS-encoded high quality photographs, video, or audio. Additionally, the role of "human-in-the-loop", in which individuals or teams using advanced human computer interface (HCI) tools such as stereoscopic 3D visualization, haptic interfaces, or aural "sonification" interfaces can help to effectively engage the innate human capability to perform pattern matching, anomaly identification, and semantic-based contextual reasoning to interpret an evolving situation. The Pennsylvania State University is participating in a Multi-disciplinary University Research Initiative (MURI) program funded by the U.S. Army Research Office to investigate fusion of hard and soft data in counterinsurgency (COIN) situations. In addition to the importance of this research for Intelligence Preparation of the Battlefield (IPB), many of the same challenges and techniques apply to health and medical informatics, crisis management, crowd-sourced "citizen science", and monitoring environmental concerns. One of the key challenges that we have encountered is the development of data formats, protocols, and methodologies to establish an information architecture and framework for the effective capture, representation, transmission, and storage of the vastly heterogeneous data and accompanying metadata -- including capabilities and characteristics of human observers, uncertainty of human observations, "soft" contextual data, and information pedigree. This

关键词： Service oriented architecture (SOA)

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Image fusion for remote sensing using fast, large-scale neuroscience models

Image fusion for remote sensing using fast, large-scale neur...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Brumby, Steven P. Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9780819486387

We present results with large-scale neuroscience-inspired models for feature detection using multi-spectral visible/infrared satellite imagery. We describe a model using an artificial neural network architecture and learning rules to build sparse scene representations over an adaptive dictionary, fusing spectral and spatial textural characteristics of the objects of interest. Our results with fast codes implemented on clusters of graphical processor units (GPUs) suggest that visual cortex models are a promising approach to practical pattern recognition problems in remote sensing, even for datasets using spectral bands not found in natural visual systems.

关键词： Visual cortex sparse coding artificial neural network

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Mask Pyramid Methodology for Enhanced Localization in Image fusion and Enhancement

Mask Pyramid Methodology for Enhanced Localization in Image ...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Zhang, David C. Chai, Sek van der Wal, Gooitzen Berends, David Sufi, Azhar Buchanan, Greg Piacentino, Michael Burt, Peter J. SRI Int Sarnoff Princeton NJ 08540 USA

ISBN: (纸本)9780819486387

Image fusion is a process that combines regions of images from different sources into a single fused image based on a salience selection rule for each region. In this paper, we proposed an algorithmic approach using a mask pyramid to better localize the selection process. A mask pyramid operates in different scales of the image to improve the fused image quality beyond a global selection rule. The proposed approach offers a generic methodology for applications in image enhancement, high dynamic range compression, depth of field extension, and image blending. The mask pyramid can also be encoded for intelligent analysis of source imagery. Several examples of this mask pyramid method are provided to demonstrate its performance in a variety of applications. A new embedded system architecture that builds upon the Acadia (R) II Vision Processor is proposed.

关键词： image fusion exposure fusion high dynamic range imaging focus fusion image blending noise reduction image enhancement

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INFORM Lab: a testbed for high-level information fusion and resource management

INFORM Lab: a testbed for high-level information fusion and ...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Valin, Pierre Guitouni, Adel Bosse, Eloi Wehn, Hans Happe, Jens Def R&D Canada Valcartier Decis Support Syst Sect C2 2459 Blvd Pie 11 Nord Quebec City PQ G3J 1X5 Canada MacDonald Dettwiler & Associates Ltd Richmond BC V6V 2J3 Canada

ISBN: (纸本)9780819486387

DRDC Valcartier and MDA have created an advanced simulation testbed for the purpose of evaluating the effectiveness of Network Enabled Operations in a Coastal Wide Area Surveillance situation, with algorithms provided by several universities. This INFORM Lab testbed allows experimenting with high-level distributed information fusion, dynamic resource management and configuration management, given multiple constraints on the resources and their communications networks. This paper describes the architecture of INFORM Lab, the essential concepts of goals and situation evidence, a selected set of algorithms for distributed information fusion and dynamic resource management, as well as auto-configurable information fusion architectures. The testbed provides general services which include a multilayer plug-and-play architecture, and a general multi-agent framework based on John Boyd's OODA loop. The testbed's performance is demonstrated on 2 types of scenarios/vignettes for 1) cooperative search-and-rescue efforts, and 2) a noncooperative smuggling scenario involving many target ships and various methods of deceit. For each mission, an appropriate subset of Canadian airborne and naval platforms are dispatched to collect situation evidence, which is fused, and then used to modify the platform trajectories for the most efficient collection of further situation evidence. These platforms are fusion nodes which obey a Command and Control node hierarchy.

关键词： surveillance information fusion distributed fusion software agents simulation testbed

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A relaxed fusion of information from real and synthetic images to predict complex behavior

A relaxed fusion of information from real and synthetic imag...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Lyons, Damian M. Benjamin, D. Paul Fordham Univ Robot & Comp Vis Lab Bronx NY 10458 USA Pace Univ Dept Comp Sci New York NY 10023 USA

ISBN: (纸本)9780819486387

An important component of cognitive robotics is the ability to mentally simulate physical processes and to compare the expected results with the information reported by a robot's sensors. In previous work, we have proposed an approach that integrates a 3D game-engine simulation into the robot control architecture. A key part of that architecture is the Match-Mediated Difference (MMD) operation, an approach to fusing sensory data and synthetic predictions at the image level. The MMD operation insists that simulated and predicted scenes are similar in terms of the appearance of the objects in the scene. This is an overly restrictive constraint on the simulation since parts of the predicted scene may not have been previously viewed by the robot. In this paper we propose an extended MMD operation that relaxes the constraint and allows the real and synthetic scenes to differ in some features but not in (selected) other features. Image difference operations that allow a real image and synthetic image generated from an arbitrarily colored graphical model of a scene to be compared. Scenes with the same content show a zero difference. Scenes with varying foreground objects can be controlled to compare the color, size and shape of the foreground.

关键词： cognitive robotics problem-solving simulation computer vision sensory fusion

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multisource information fusion for Logistics

Multisource Information Fusion for Logistics

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Woodley, Robert Petrov, Plamen Noll, Warren 21st Century Syst Inc Omaha NE 68106 USA

ISBN: (纸本)9780819486387

Current Army logistical systems and databases contain massive amounts of data that need an effective method to extract actionable information. The databases do not contain root cause and case-based analysis needed to diagnose or predict breakdowns. A system is needed to find data from as many sources as possible, process it in an integrated fashion, and disseminate information products on the readiness of the fleet vehicles. 21st Century Systems, Inc. introduces the Agent-Enabled Logistics Enterprise Intelligence System (AELEIS) tool, designed to assist logistics analysts with assessing the availability and prognostics of assets in the logistics pipeline. AELEIS extracts data from multiple, heterogeneous data sets. This data is then aggregated and mined for data trends. Finally, data reasoning tools and prognostics tools evaluate the data for relevance and potential issues. Multiple types of data mining tools may be employed to extract the data and an information reasoning capability determines what tools are needed to apply them to extract information. This can be visualized as a push-pull system where data trends fire a reasoning engine to search for corroborating evidence and then integrate the data into actionable information. The architecture decides on what reasoning engine to use (i.e., it may start with a rule-based method, but, if needed, go to condition based reasoning, and even a model-based reasoning engine for certain types of equipment). Initial results show that AELEIS is able to indicate to the user of potential fault conditions and root-cause information mined from a database.

关键词： Expert systems information fusion Knowledge based systems Vehicle health monitoring Data reasoning Intelligent agent software Data mining and clustering

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Technical Overview of the Sarnoff Acadia II Vision Processor

Technical Overview of the Sarnoff Acadia II Vision Processor

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2010

作者： van der Wal, Gooitzen S. Sarnoff Corp Princeton NJ 08540 USA

ISBN: (纸本)9780819481740

The Sarnoff Acadia (R) II is a powerful vision processing SoC (System-on-a-Chip) that was specifically developed to support advanced vision applications where system size, weight and/or power are severely constrained. This paper, targeted at vision system developers, presents a detailed technical overview of the Acadia (R) II, highlighting its architecture, processing capabilities, memory and peripheral interfaces. All major subsystems will be covered, including: video preprocessing, specialized vision processing cores for multi-spectral image fusion, multi-resolution contrast normalization, noise coring, image warping, and motion estimation. Application processing via the MPCore (R), an integrated set of four ARM (R) 11 floating point processors with associated peripheral interfaces is presented in detail. The paper will emphasize the programmability of the Acadia (R) II, while describing its ability to provide state-of-the-art real-time image processing in a small, power optimized package.*

关键词： Real-time Multiresolution Vision processing Image fusion Stabilization Embedded Processing Multicore multi-spectral

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