Data fusion on a distributed heterogeneous sensor network

Lamborn, Peter; Williams, Pamela J.

doi:10.1117/12.665850

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…The automated deployment of large numbers of sensing nodes and interpretations pose several new technical problems and challenges. Diverse applications exist for this technology including precision agriculture, livestock tracking, traffic monitoring and geophysical and environmental monitoring (Lamborn and Williams 2006).…”

Section: Distributed Web-sensor For Data Fusionmentioning

confidence: 99%

Multi-source remote sensing data fusion: status and trends

Zhang

2010

International Journal of Image and Data Fusion

608

325

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With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fusion methods have mainly focused on optical data fusion; high-level fusion includes feature level and decision level fusion of multi-source data, such as synthetic aperture radar, optical images, LiDAR and other types of data. Finally, this article summarises several trends tending to broaden the application of multi-source data fusion.

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Section: Distributed Web-sensor For Data Fusionmentioning

confidence: 99%

Multi-source remote sensing data fusion: status and trends

Zhang

2010

International Journal of Image and Data Fusion

608

325

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“…International Journal of Distributed Sensor Networks 3 (1) Randomly initialize the set of endmembers (0) from the dimensionality reduced hyperspectral data. (0) = { 1 (0) , 2 (0) , .…”

Section: Fast Endmember Extraction Based On Gpusmentioning

confidence: 99%

“…Multisensor image data fusion in remote sensing is a kind of collaborative image processing technology for sensor networks, which utilize the consistency and complementarity of different sensors' image data to assess accurately [1]. Hyperspectral imaging sensor becomes increasingly important in multisensor collaborative observation.…”

Section: Introductionmentioning

confidence: 99%

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Jie

et al. 2013

International Journal of Distributed Sensor Networks

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Hyperspectral imaging sensor becomes increasingly important in multisensor collaborative observation. The spectral mixture problem seriously influences the efficiency of hyperspectral data exploitation, and endmember extraction is one of the key issues. Due to the high computational cost of algorithm and massive quantity of the hyperspectral sensor data, high-performance computing is extremely demanded for those scenarios requiring real-time response. A method of parallel optimization for the wellknown N-FINDR algorithm on graphics processing units (NFINDR-GPU) is proposed to realize fast endmember extraction for massive hyperspectral sensor data in this paper. The implements of the proposed method are described and evaluated using compute unified device architecture (CUDA) based on NVIDA Quadra 600 and Telsa C2050. Experimental results show the effectiveness of NFINDR-GPU. The parallel algorithm is stable for different image sizes, and the average speedup is over thirty times on Telsa C2050, which satisfies the real-time processing requirements.

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“…Next, the 2D and 3D features are combined, which improves the user's face recognition. Lambort et al [5] introduced an intelligent system that consists of several heterogeneous sensors with a weighted voting algorithm for computing the final result. To avoid a large number of false alarms, the results from several sensors were integrated into the classification, thus creating an advance situational awareness.…”

Section: Related Workmentioning

confidence: 99%

Improving user verification by implementing an agent-based security system

Dovgan

Kaluža²,

Tušar³

et al. 2010

Journal of Ambient Intelligence and Smart Environments

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This paper presents an agent-based, high-level security system for user verification. The system verifies a user's identity by processing data from several low-lever sensors mounted at an entry point combined with knowledge of the user's past behavior. The data from a new entry are processed by several agents, which store the knowledge in an ontology. A single agent's classifications are integrated into the overall decision. The system successfully detects intruders, even when they optimally fake low-level sensors, e.g. fingerprints, and regular personnel under the influence of drugs.

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Data fusion on a distributed heterogeneous sensor network

Cited by 9 publications

References 7 publications

Multi-source remote sensing data fusion: status and trends

Multi-source remote sensing data fusion: status and trends

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

Improving user verification by implementing an agent-based security system

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