&lt;title&gt;Scene classification and segmentation using multispectral sensor fusion implemented with neural networks&lt;/title&gt;

Lazofson, Laurence E.; Kuzma, Thomas J.

doi:10.1117/12.152527

Cited by 4 publications

(2 citation statements)

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“…A PCA-based technique measuring pixel variances in local neighborhoods is used in [8]. Pixel-level combinations of spatial interest images using Boolean and fuzzy-logic operators are proposed in [14], and a neural networks model for pixel-level classification is used in [25].…”

Section: Fusionmentioning

confidence: 99%

Background-subtraction using contour-based fusion of thermal and visible imagery

Davis

Sharma

2007

Computer Vision and Image Understanding

386

217

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Section: Fusionmentioning

confidence: 99%

Background-subtraction using contour-based fusion of thermal and visible imagery

Davis

Sharma

2007

Computer Vision and Image Understanding

386

217

View full text Add to dashboard Cite

“…A PCA-based technique measuring pixel variances in local neighborhoods is used in [7]. Pixel-level combinations of spatial interest images using Boolean and fuzzy-logic operators are proposed in [11], and a neural networks model for pixel-level classification is used in [16].…”

Section: Outlinementioning

confidence: 99%

Feature-Level Fusion for Object Segmentation Using Mutual Information

Sharma

Davis

Augmented Vision Perception in Infrared

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Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms

Preston

Bergman

Gorenflo

et al. 1994

IEEE Aerosp. Electron. Syst. Mag.

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Battelle scientists have assembled a reconfigurable multispectral imaging and classification system which can be taken into the field to support automated real-time targetbackground discrimination. The system may be used for a variety of applications including environmental remote sensing, industrial inspection and medical imaging. This paper discusses hard tactical target and runway detection applications performed with the multispectral system. multispectral imaging electro-optical (EO) sensor suite and a real-time digital data collection and data fusion image processor. The EO sensor suite, able to collect imagery in 12 distinct wavebands from the ultraviolet (UV) through the long wave infrared (LWIR), consists of five charge-coupled deviceThe Battelle-developed system consists of a passive, (CCD) cameras and two thermal IR imagers integrated on a common portable platform. The data collection and processing system consists of video switchers, recorders and a real-time sensor fusiordclassification hardware system which combines any three input wavebands to perform real-time data fusion by applying "look-up tables," derived from tailored neural network algorithm, to classify the imaged scene pixel by pixel. The result is then visualized in a video format on a full color, 9-inch, active matrix Liquid Crystal Display ( E D ) .A variety of classification algorithms including artificial neural networks and data clustering techniques were successfully optimized to perform pixel-level classification of imagery in complex scenes comprised of tactical targets, buildings, roads, aircraft runways, and vegetation. Algorithms implemented included unsupervised maximum likelihood, Linde Buzo Gray, and "fuzzy" clustering algorithms along with Multilayer Perceptron and Learning Vector Quantization (LVQ) neural networks. Supervised clustering of the data was also evaluated. To assess classification robustness, algorithms were tested on imagery recorded over broad periods of time throughout the day. Results were excellent, indicating that scene classification is achievable despite temporal signature variations. Waveband saliency analyses were performed to I3

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<title>Scene classification and segmentation using multispectral sensor fusion implemented with neural networks</title>

Cited by 4 publications

References 0 publications

Background-subtraction using contour-based fusion of thermal and visible imagery

Background-subtraction using contour-based fusion of thermal and visible imagery

Feature-Level Fusion for Object Segmentation Using Mutual Information

Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms

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