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Unsupervised Data fusion With Deeper Perspective: A Novel multisensor Deep Clustering Algorithm

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 284-296页

作者： Shahi, Kasra Rafiezadeh Ghamisi, Pedram Rasti, Behnood Scheunders, Paul Gloaguen, Richard Helmholtz Zentrum Dresden Rossendorf Helmholtz Inst Freiberg Resource Technol D-09599 Freiberg Germany Univ Antwerp Dept Phys Imec Visionlab B-2000 Antwerp Belgium

The ever-growing developments in technology to capture different types of image data [e.g., hyperspectral imaging and light detection and ranging (LiDAR)-derived digital surface model (DSM)], along with new processing techniques, have led to a rising interest in imaging applications for Earth observation. However, analyzing such datasets in parallel, remains a challenging task. In this article, we propose a multisensor deep clustering (MDC) algorithm for the joint processing of multisource imaging data. The architecture of MDC is inspired by autoencoder (AE)-based networks. The MDC paradigm includes three parallel networks, a spectral network using an autoencoder structure, a spatial network using a convolutional autoencoder structure, and lastly, a fusion network that reconstructs the concatenated image information from the concatenated latent features from the spatial and spectral network. The proposed algorithm combines the reconstruction losses obtained by the aforementioned networks to optimize the parameters (i.e., weights and bias) of all three networks simultaneously. To validate the performance of the proposed algorithm, we use two multisensor datasets from different applications (i.e., geological and rural sites) as benchmarks. The experimental results confirm the superiority of our proposed deep clustering algorithm compared to a number of state-of-the-art clustering algorithms. The code will be available at [Online]. Available: https://***/Kasra2020/MDC.

关键词： Clustering algorithms Image reconstruction Feature extraction Decoding Spatial resolution Imaging Hyperspectral imaging Autoencoder (AE) convolutional autoencoder (CAE) deep learning multisensor data fusion remote sensing

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PASES: Combining radar and video sensing for improved pedestrian safety

PASES: Combining radar and video sensing for improved pedest...

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

作者： Chenault, David B. Edmondson, Richard P. Polaris Sensor Technol Inc 200 West Side SqSuite 320 Huntsville AL 35801 USA

ISBN: (纸本)9781510601130

Polaris Sensor Technologies reports on the development of Pedestrian Automated System for Enforcement and Safety (PASES), a radar and video based system used to monitor vehicle and pedestrian traffic with the intent of improving pedestrian safety. Data is fused from a system of multiple sensors and multiple sensor modalities to identify vehicular violations of pedestrian right of way. A focus was placed on the selection of low cost COTS sensors to make the system more widely available to state and local DOTs with limited budgets. applications include automated enforcement, adaptive traffic control, and improved intersection and crosswalk design based on high quality data available for traffic engineering. We discuss early results with high fidelity sensors, and the performance trades made in order to make the system affordable. A discussion of the system processing architecture is included which highlights the treatment of each sensor data type, and the means of combining the processed data products into state information related to traffic incidents involving vehicles and pedestrians.

关键词： Pedestrian radar video tracking sensor fusion

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Considerations for Multiple Hypothesis Correlation on Tactical Platforms

Considerations for Multiple Hypothesis Correlation on Tactic...

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

作者： Thomas, Alan M. Turpen, James E. Georgia Inst Technol Georgia Tech Res Inst Atlanta GA 30318 USA

ISBN: (纸本)9780819495471

Tactical platforms benefit greatly from the fusion of tracks from multiple sources in terms of increased situation awareness. As a necessary precursor to this track fusion, track-to-track association, or correlation, must first be performed. The related measurement-to-track fusion problem has been well studied with multiple hypothesis tracking and multiple frame assignment methods showing the most success. The track-to-track problem differs from this one in that measurements themselves are not available but rather track state update reports from the measuring sensors. Multiple hypothesis, multiple frame correlation systems have previously been considered;however, their practical implementation under the constraints imposed by tactical platforms is daunting. The situation is further exacerbated by the inconvenient nature of reports from legacy sensor systems on bandwidth-limited communications networks. In this paper, consideration is given to the special difficulties encountered when attempting the correlation of tracks from legacy sensors on tactical aircraft. Those difficulties include the following: covariance information from reporting sensors is frequently absent or incomplete;system latencies can create temporal uncertainty in data;and computational processing is severely limited by hardware and architecture. Moreover, consideration is given to practical solutions for dealing with these problems in a multiple hypothesis correlator.

关键词： Data Association Multiple Hypothesis Correlation Multiple Hypothesis Tracking Avionics information fusion Sensors

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Affordable Non-Traditional Source Data Mining for Context Assessment to Improve Distributed fusion System Robustness

Affordable Non-Traditional Source Data Mining for Context As...

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

作者： Bowman, Christopher Haith, Gary Data Fusion & Neural Networks LLC (United States) Georgia Tech Research Institute (United States) Arctan Group LLC (United States)

ISBN: (纸本)9780819495471

This paper describes methods to affordably improve the robustness of distributed fusion systems by opportunistically leveraging non-traditional data sources. Adaptive methods help find relevant data, create models, and characterize the model quality. These methods also can measure the conformity of this non-traditional data with fusion system products including situation modeling and mission impact prediction. Non-traditional data can improve the quantity, quality, availability, timeliness, and diversity of the baseline fusion system sources and therefore can improve prediction and estimation accuracy and robustness at all levels of fusion. Techniques are described that automatically learn to characterize and search non-traditional contextual data to enable operators integrate the data with the high-level fusion systems and ontologies. These techniques apply the extension of the Data fusion & Resource Management Dual Node Network (DNN) technical architecture at Level 4. The DNN architecture supports effectively assessment and management of the expanded portfolio of data sources, entities of interest, models, and algorithms including data pattern discovery and context conformity. Affordable model-driven and data-driven data mining methods to discover unknown models from non-traditional and 'big data' sources are used to automatically learn entity behaviors and correlations with fusion products, [14 and 15]. This paper describes our context assessment software development, and the demonstration of context assessment of non-traditional data to compare to an intelligence surveillance and reconnaissance fusion product based upon an IED POIs workflow.

关键词： Data fusion Data modeling Sensors Systems modeling Intelligence systems Data processing Performance modeling Control systems Data acquisition Data mining

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Enhancing situational awareness by means of visualization and information integration of sensor networks

Enhancing situational awareness by means of visualization an...

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

作者： Timonen, J. Vankka, J. Finnish Natl Def Univ Dept Mil Technol POB 7 FI-00861 Helsinki Finland

ISBN: (纸本)9780819495471

This paper presents a solution for information integration and sharing architecture, which is able to receive data simultaneously from multiple different sensor networks. Creating a Common Operational Picture (COP) object along with the base map of the building plays a key role in the research. The object is combined with desired map sources and then shared to the mobile devices worn by soldiers in the field. The sensor networks we used focus on location techniques indoors, and a simple set of symbols is created to present the information, as an addition to NATO APP6B symbols. A core element in this research is the MUSAS (Mobile Urban Situational Awareness System), a demonstration environment that implements central functionalities. information integration of the system is handled by the Internet Connection Engine (Ice) middleware, as well as the server, which hosts COP information and maps. The entire system is closed, such that it does not need any external service, and the information transfer with the mobile devices is organized by a tactical 5 GHz WLAN solution. The demonstration environment is implemented using only commercial off-theshelf (COTS) products. We have presented a field experiment event in which the system was able to integrate and share real time information of a blue force tracking system, received signal strength indicator (RSSI) based intrusion detection system, and a robot using simultaneous location and mapping technology (SLAM), where all the inputs were based on real activities. The event was held in a training area on urban area warfare.

关键词： Common Operational Picture information integration urban warfare wireless sensor platform Mobile Urban Area Situational Awareness System Situational Awareness

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Scalable sensor management for automated fusion and tactical reconnaissance

Scalable sensor management for automated fusion and tactical...

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

作者： Walls, Thomas J. Wilson, Michael L. Partridge, Darin C. Haws, Jonathan R. Jensen, Mark D. Johnson, Troy R. Petersen, Brad D. Sullivan, Stephanie W. Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)9780819495471

The capabilities of tactical intelligence, surveillance, and reconnaissance (ISR) payloads are expanding from single sensor imagers to integrated systems-of-systems architectures. Increasingly, these systems-of-systems include multiple sensing modalities that can act as force multipliers for the intelligence analyst. Currently, the separate sensing modalities operate largely independent of one another, providing a selection of operating modes but not an integrated intelligence product. We describe here a Sensor Management System (SMS) designed to provide a small, compact processing unit capable of managing multiple collaborative sensor systems on-board an aircraft. Its purpose is to increase sensor cooperation and collaboration to achieve intelligent data collection and exploitation. The SMS architecture is designed to be largely sensor and data agnostic and provide flexible networked access for both data providers and data consumers. It supports pre-planned and ad-hoc missions, with provisions for on-demand tasking and updates from users connected via data links. Management of sensors and user agents takes place over standard network protocols such that any number and combination of sensors and user agents, either on the local network or connected via data link, can register with the SMS at any time during the mission. The SMS provides control over sensor data collection to handle logging and routing of data products to subscribing user agents. It also supports the addition of algorithmic data processing agents for feature/target extraction and provides for subsequent cueing from one sensor to another. The SMS architecture was designed to scale from a small UAV carrying a limited number of payloads to an aircraft carrying a large number of payloads. The SMS system is STANAG 4575 compliant as a removable memory module (RMM) and can act as a vehicle specific module (VSM) to provide STANAG 4586 compliance (level-3 interoperability) to a non-compliant sensor system. Th

关键词： multi-INT data fusion sensing modality sensor management network sensor tasking automation algorithmic processing scalable systems-of-systems

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Spectral and Spatial Algorithm architecture for Classification of Hyperspectral and LIDAR

Spectral and Spatial Algorithm Architecture for Classificati...

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

作者： Rand, Robert S. Khuon, Timothy S. Natl Geospatial Intelligence Agcy Off Innovis Bas & Appl Res Springfield VA 22153 USA

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.

关键词： Sensor fusion architecture Neural Net HSI LIDAR Classification Mean-shift Stochastic Expectation-Maximization Imagery Back-propagation Perceptron Probability of detection and false alarm

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

Homeland situation awareness through mining and fusing heter...

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

作者： Digioia, Giusj Panzieri, Stefano Roma Tre Univ Comp & Automat Dept I-00146 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.

关键词： Situation Awareness Data Mining Hidden Markov Models Agile Models

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

Navigation of uncertain terrain by fusion of information fro...

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

作者： Lyons, Damian M. Nirmal, Paramesh 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)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.

关键词： cognitive robotics navigation sensory fusion

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Advances in data representation for hard/soft information fusion

Advances in data representation for hard/soft information fu...

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

作者： Rimland, Jeffrey C. Coughlin, Dan Hall, David L. Graham, Jacob L. Penn State Univ Coll IST 101IST Bldg University Pk PA 16802 USA Penn State Univ Paterno Lib University Pk PA 16802 USA

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. Thi

关键词： hard and soft information fusion hybrid cognition service oriented architecture SOA ESB

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