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Title: Spatial clustering of pixels of a multispectral image

Patent ·
OSTI ID:1150628

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.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC52-07NA27344
Assignee:
Lawrence Livermore National Security, LLC (Livermore, CA)
Patent Number(s):
8,811,754
Application Number:
13/353,728
OSTI ID:
1150628
Country of Publication:
United States
Language:
English

References (10)

Contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation conference October 1997
Adaptive Threshold for Spectral Matching of Hyperspectral Data journal June 2001
Bayesian Approach With Hidden Markov Modeling and Mean Field Approximation for Hyperspectral Data Analysis journal February 2008
Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms conference May 2008
Hierarchical clustering approach for unsupervised image classification of hyperspectral data conference January 2004
Segmentation of multispectral remote sensing images based on ant colony optimization algorithm 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
Analysis of hyper-spectral data derived from an imaging Fourier transform: A statistical perspective report January 1996

Cited By (1)

Spatial clustering of pixels of a multispectral image patent August 2014

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97 MATHEMATICS AND COMPUTING