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Title: Spatial clustering of pixels of a multispectral image

Abstract

A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.

Inventors:
Issue Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1150628
Patent Number(s):
8811754
Application Number:
13/353,728
Assignee:
Lawrence Livermore National Security, LLC (Livermore, CA)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Conger, James Lynn. Spatial clustering of pixels of a multispectral image. United States: N. p., 2014. Web.
Conger, James Lynn. Spatial clustering of pixels of a multispectral image. United States.
Conger, James Lynn. Tue . "Spatial clustering of pixels of a multispectral image". United States. https://www.osti.gov/servlets/purl/1150628.
@article{osti_1150628,
title = {Spatial clustering of pixels of a multispectral image},
author = {Conger, James Lynn},
abstractNote = {A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2014},
month = {8}
}

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Works referenced in this record:

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conference, October 1997


Adaptive Threshold for Spectral Matching of Hyperspectral Data
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conference, January 2009


Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
journal, August 2009


K-means reclustering: an alternative approach to automatic target cueing in hyperspectral images
conference, July 2002


Method of compressing hyperspectral images and detecting spectral anomalies
conference, January 2002