Spectral Morphology for Feature Extraction from Multi- and Hyper-spectral Imagery.

Harvey, N R; Porter, R B

doi:10.1117/12.602747

Title: Spectral Morphology for Feature Extraction from Multi- and Hyper-spectral Imagery.

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Abstract

For accurate and robust analysis of remotely-sensed imagery it is necessary to combine the information from both spectral and spatial domains in a meaningful manner. The two domains are intimately linked: objects in a scene are defined in terms of both their composition and their spatial arrangement, and cannot accurately be described by information from either of these two domains on their own. To date there have been relatively few methods for combining spectral and spatial information concurrently. Most techniques involve separate processing for extracting spatial and spectral information. In this paper we will describe several extensions to traditional morphological operators that can treat spectral and spatial domains concurrently and can be used to extract relationships between these domains in a meaningful way. This includes the investgation and development of suitable vector-ordering metrics and machine-learning-based techniques for optimizing the various parameters of the morphological operators, such as morphological operator, structuring element and vector ordering metric. We demonstrate their application to a range of multi- and hyper-spectral image analysis problems.

Authors:

Harvey, N R ^[1]; Porter, R B ^[2]

Neal R.
Reid B.

Publication Date:: Sat Jan 01 00:00:00 EST 2005

Research Org.:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 977957

Report Number(s):: LA-UR-05-1610
Journal ID: ISSN 0277-786X; TRN: US201012%%593

Resource Type:: Conference

Resource Relation:: Journal Volume: 5806; Conference: Submitted to: SPIE Defense and Security Symposium 2005, March 2005, Orlando, Florida

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICAL METHODS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; SPECTRA; IMAGE PROCESSING; MORPHOLOGY; REMOTE SENSING; SPACE DEPENDENCE

Citation Formats


                    Harvey, N R, and Porter, R B. Spectral Morphology for Feature Extraction from Multi- and Hyper-spectral Imagery..  United States: N. p., 2005. 
        Web.  doi:10.1117/12.602747.

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                    Harvey, N R, & Porter, R B. Spectral Morphology for Feature Extraction from Multi- and Hyper-spectral Imagery..  United States.  https://doi.org/10.1117/12.602747

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                    Harvey, N R, and Porter, R B. 2005.  
        "Spectral Morphology for Feature Extraction from Multi- and Hyper-spectral Imagery.".  United States.  https://doi.org/10.1117/12.602747.  https://www.osti.gov/servlets/purl/977957.

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@article{osti_977957,

  title        = {Spectral Morphology for Feature Extraction from Multi- and Hyper-spectral Imagery.},

  author       = {Harvey, N R and Porter, R B},

  abstractNote = {For accurate and robust analysis of remotely-sensed imagery it is necessary to combine the information from both spectral and spatial domains in a meaningful manner. The two domains are intimately linked: objects in a scene are defined in terms of both their composition and their spatial arrangement, and cannot accurately be described by information from either of these two domains on their own. To date there have been relatively few methods for combining spectral and spatial information concurrently. Most techniques involve separate processing for extracting spatial and spectral information. In this paper we will describe several extensions to traditional morphological operators that can treat spectral and spatial domains concurrently and can be used to extract relationships between these domains in a meaningful way. This includes the investgation and development of suitable vector-ordering metrics and machine-learning-based techniques for optimizing the various parameters of the morphological operators, such as morphological operator, structuring element and vector ordering metric. We demonstrate their application to a range of multi- and hyper-spectral image analysis problems.},

  doi          = {10.1117/12.602747},

  url          = {https://www.osti.gov/biblio/977957},
  journal      = {},

  issn         = {0277-786X},

  number       = ,

  volume       = 5806,

  place        = {United States},

  year         = {Sat Jan 01 00:00:00 EST 2005},

  month        = {Sat Jan 01 00:00:00 EST 2005}

}

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