&lt;title&gt;Neural networks for distributed sensor data fusion: the Firefly experiment&lt;/title&gt;

Levine, Robert Y.; Khuon, Timothy S.

doi:10.1117/12.57912

Cited by 3 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section we review some of these results, where successful data fusion by Chair and Varshney [5] in terms of probability of detection and probability of false alarms were shown to be achieved similarly by a cascaded neural net by Levine and Khuon [6][7][8][9]. The data fusion rule for a binary decision was obtained within the distributed sensor processing architecture.…”

Section: Motivation For Fusion Neural Netmentioning

confidence: 95%

“…The higher noise deviation 1  is sensor dependent so that each SNN is trained on a different pair Extending the architecture to M sensors, a cascaded back propagation neural net for M sensors as shown in Figure 4 was used for computing data fusion for Q decisions [8,9]. In this case, there are only two decisions TRANSITION and NO-TRANSITION.…”

Section: Motivation For Fusion Neural Netmentioning

confidence: 99%

“…This is often called a Sensor Neural Network (SNN). The fusion of two SXOR networks is called a Fusion Neural Net (FNN), and one such FNN architecture that has been used in previous work to test for performance [8,9] is shown in Figure 3.…”

Section: Motivation For Fusion Neural Netmentioning

confidence: 99%

“…In Levine and Khuon [8,9], the outputs from the network were benchmarked. The higher noise deviation 1  is sensor dependent so that each SNN is trained on a different pair Extending the architecture to M sensors, a cascaded back propagation neural net for M sensors as shown in Figure 4 was used for computing data fusion for Q decisions [8,9].…”

Section: Motivation For Fusion Neural Netmentioning

confidence: 99%

See 3 more Smart Citations

Spectral and spatial algorithm architecture for classification of hyperspectral and LIDAR

Rand

Khuon

2012

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Motivation For Fusion Neural Netmentioning

confidence: 95%

Section: Motivation For Fusion Neural Netmentioning

confidence: 99%

Section: Motivation For Fusion Neural Netmentioning

confidence: 99%

Section: Motivation For Fusion Neural Netmentioning

confidence: 99%

See 2 more Smart Citations

Spectral and spatial algorithm architecture for classification of hyperspectral and LIDAR

Rand

Khuon

2012

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our motivation comes from observing the successful performance of NNSF methods in scenarios relevant to our application; specifically, the successful data fusion by Chair and Varshney 31 was shown to be achieved similarly (in terms of probability of detection and probability of false alarms) by a cascaded neural net by Levine and Khuon. [32][33][34][35][36] Successful performance of neural networks was achieved for nonlinear detection and classification problems for imagery data fusion in this research. A detailed discussion of the FNN for data/ sensor fusion can be found in Refs.…”

Section: Spectral-spatial Neural Net Sensor Fusionmentioning

confidence: 96%

Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

2016

View full text Add to dashboard Cite

Abstract. A proposed framework using spectral and spatial information is introduced for neural net multisensor data fusion. This consists of a set of independent-sensor neural nets, one for each sensor (type of data), 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. During the initial processing, three-dimensional (3-D) point cloud data (PCD) are segmented using a multidimensional mean-shift algorithm into clustered objects. Concurrently, multiband spectral imagery data (multispectral or hyperspectral) are spectrally segmented by the stochastic expectation-maximization into a cluster map containing (spectral-based) pixel classes. For the proposed 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 success of the approach in utilizing sensor synergism for an enhanced classification is demonstrated for the specific case of classifying hyperspectral imagery and PCD extracted from LIDAR, obtained from an airborne data collection over the campus

show abstract

A method of trajectory inverse estimation for space targets and performance analysis

Qiu

Liu

2013

2013 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference

View full text Add to dashboard Cite

<title>Neural networks for distributed sensor data fusion: the Firefly experiment</title>

Cited by 3 publications

References 0 publications

Spectral and spatial algorithm architecture for classification of hyperspectral and LIDAR

Spectral and spatial algorithm architecture for classification of hyperspectral and LIDAR

Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

A method of trajectory inverse estimation for space targets and performance analysis

Contact Info

Product

Resources

About