Optimal band selection for target detection with a LWIR multispectral imager

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 most consistent detection separation scores on average. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Hyperspectral to multispectral: optimal selection of mission-relevant bands using machine learning

“…These authors implemented both SVMs and random forests as classifiers and showed results for the Pavia University dataset and the Indian Pines dataset. Also in 2022, a rather comprehensive study was published by Zelinski et al 25 from Lawrence Livermore National Lab. These authors compared the band-selection results from three different integer-programming algorithms against those from a brute-force search performed on a supercomputer.…”

Multi-spectral uncooled infrared sensors fabricated using encapsulated metal-insulator-metal plasmonic metamaterial absorbers

“…Multispectral uncooled infrared (IR) sensors are attracting interest for use in applications such as gas analysis, hazardous material recognition, biological analysis, and multi-wavelength imaging. [1][2][3] However, conventional sensors require additional filters or a resonant structure in the vertical direction, [4][5][6] which causes high cost and requires complicated integration procedures. To address this challenge, plasmonic metamaterial absorbers (PMAs) such as plasmonic crystals (PCs), [7][8][9] metal-insulator-metal (MIM), [10][11][12] and mushroom-type PMAs [13][14][15][16] are being widely investigated as IR absorbers.…”