Optimal band selection for target detection with a LWIR multispectral imager

Zelinski, Michael; Mastin, Andrew; Castillo, Vic; Yoxall, Brian

doi:10.1117/1.jrs.16.026505

Title: Optimal band selection for target detection with a LWIR multispectral imager

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Abstract

Multispectral imaging can offer many benefits in cost, complexity, resolution, size, weight, and power, relative to hyperspectral imaging. When designing a multispectral system, spectral bandpasses can be selected using optimization algorithms configured to maximally separate target detection scores between target and background regions. A hyperspectral image (HSI) can serve as the source of data from which band groupings can be tested for optimality. The output of an adaptive cosine estimator target detection algorithm is used in an objective function. Three optimization algorithms are compared: particle swarm, dual annealing, and differential evolution. A global optimum is also found using a brute force approach on the Livermore Computing Syrah supercomputer. Three materials are investigated: calcite, gypsum, and limestone. This is done for 3-, 4-, and 5-band systems. The data originate from a longwave infrared HSI of a material display board. The optimization algorithms were run 30 times for every scenario. Performance statistics (maximum, minimum, mean, standard deviation, and median) based on the separation values are given. Additional characterization was performed using receiver operator characteristic (ROC) curves and the area under the ROC curve. While good performance was obtained for the three optimization algorithms, the dual annealing algorithm produced the highest and mostmore »« less

Authors:

Zelinski, Michael ^[1]; Mastin, Andrew ^[1]; Castillo, Vic ^[1]; Yoxall, Brian ^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Publication Date:: Thu Apr 14 00:00:00 EDT 2022

Research Org.:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1863177

Report Number(s):: LLNL-JRNL-825597
Journal ID: ISSN 1931-3195; 1039350

Grant/Contract Number:: AC52-07NA27344

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Applied Remote Sensing

Additional Journal Information:: Journal Volume: 16; Journal Issue: 02; Journal ID: ISSN 1931-3195

Publisher:: SPIE

Country of Publication:: United States

Language:: English

Subject:: 47 OTHER INSTRUMENTATION; multispectral; hyperspectral; longwave infrared; spectral filter design; spectral band selection; target detection; numerical optimization; supercomputing

Citation Formats


                    Zelinski, Michael, Mastin, Andrew, Castillo, Vic, and Yoxall, Brian. Optimal band selection for target detection with a LWIR multispectral imager.  United States: N. p., 2022. 
Web.  doi:10.1117/1.jrs.16.026505.

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                    Zelinski, Michael, Mastin, Andrew, Castillo, Vic, & Yoxall, Brian. Optimal band selection for target detection with a LWIR multispectral imager.  United States.  https://doi.org/10.1117/1.jrs.16.026505

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                    Zelinski, Michael, Mastin, Andrew, Castillo, Vic, and Yoxall, Brian. Thu .  
"Optimal band selection for target detection with a LWIR multispectral imager".  United States.  https://doi.org/10.1117/1.jrs.16.026505.  https://www.osti.gov/servlets/purl/1863177.

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@article{osti_1863177,

  title        = {Optimal band selection for target detection with a LWIR multispectral imager},

  author       = {Zelinski, Michael and Mastin, Andrew and Castillo, Vic and Yoxall, Brian},

  abstractNote = {Multispectral imaging can offer many benefits in cost, complexity, resolution, size, weight, and power, relative to hyperspectral imaging. When designing a multispectral system, spectral bandpasses can be selected using optimization algorithms configured to maximally separate target detection scores between target and background regions. A hyperspectral image (HSI) can serve as the source of data from which band groupings can be tested for optimality. The output of an adaptive cosine estimator target detection algorithm is used in an objective function. Three optimization algorithms are compared: particle swarm, dual annealing, and differential evolution. A global optimum is also found using a brute force approach on the Livermore Computing Syrah supercomputer. Three materials are investigated: calcite, gypsum, and limestone. This is done for 3-, 4-, and 5-band systems. The data originate from a longwave infrared HSI of a material display board. The optimization algorithms were run 30 times for every scenario. Performance statistics (maximum, minimum, mean, standard deviation, and median) based on the separation values are given. Additional characterization was performed using receiver operator characteristic (ROC) curves and the area under the ROC curve. While good performance was obtained for the three optimization algorithms, the dual annealing algorithm produced the highest and most consistent detection separation scores on average.},

  doi          = {10.1117/1.jrs.16.026505},

  journal      = {Journal of Applied Remote Sensing},

  number       = 02,

  volume       = 16,

  place        = {United States},

  year         = {Thu Apr 14 00:00:00 EDT 2022},

  month        = {Thu Apr 14 00:00:00 EDT 2022}

}

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