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 Laboratory (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)
- 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}
}
Works referenced in this record:
Contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation
conference, October 1997
- Theiler, James P.; Gisler, Galen
- Optical Science, Engineering and Instrumentation '97, SPIE Proceedings
Adaptive Threshold for Spectral Matching of Hyperspectral Data
journal, June 2001
- Schwarz, J.; Staenz, K.
- Canadian Journal of Remote Sensing, Vol. 27, Issue 3
Bayesian Approach With Hidden Markov Modeling and Mean Field Approximation for Hyperspectral Data Analysis
journal, February 2008
- Bali, N.; Mohammad-Djafari, A.
- IEEE Transactions on Image Processing, Vol. 17, Issue 2
Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms
conference, May 2008
- Duarte-Carvajalino, Julio M.; Sapiro, Guillermo; Velez-Reyes, Miguel
- SPIE Defense and Security Symposium, SPIE Proceedings
Hierarchical clustering approach for unsupervised image classification of hyperspectral data
conference, January 2004
- Sanghoon Lee, ; Crawford, M. M.
- IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004
Segmentation of multispectral remote sensing images based on ant colony optimization algorithm
conference, January 2009
- Liu, Shuo; Qiao, Yan-you; Wen, Qing-ke
- Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC '09
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
journal, August 2009
- Tarabalka, Y.; Benediktsson, J. A.; Chanussot, J.
- IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, Issue 8
K-means reclustering: an alternative approach to automatic target cueing in hyperspectral images
conference, July 2002
- Wong, Raymond S.; Ford, Gary E.; Paglieroni, David W.
- AeroSense 2002, SPIE Proceedings
Method of compressing hyperspectral images and detecting spectral anomalies
conference, January 2002
- Zherebin, Andrey A.; Tsibulkin, Leonid M.; Tihomirova, Tamara A.
- International Symposium on Remote Sensing, SPIE Proceedings
Analysis of hyper-spectral data derived from an imaging Fourier transform: A statistical perspective
report, January 1996
- Sengupta, S. K.; Clark, G. A.; Fields, D. J.
Works referencing / citing this record:
Spatial clustering of pixels of a multispectral image
patent, August 2014
- Conger, James L.
- US Patent Document 8,811,754