Unraveling Low Abundance Intimate Mixtures with Deep Learning

Published

May 14, 2019

Author(s)

Ronald G. Resmini, Kevin Christiansen, David Allen

Abstract

The high-confidence detection and identification of very low abundance, subpixel quantities of solid materials in nonlinear/intimate mixtures are still significant challenges for hyperspectral imagery (HSI) data analysis. We compare the ability of a traditional, shallow neural network (NN), deep learning with a convolutional neural network (DL/CNN), and a support vector machine (SVM) to analyze spectral signatures of nonlinear mixtures. Traditional mainstay algorithms (e.g., spectral unmixing, the matched filter) are also applied. Using a benchtop shortwave infrared (SWIR) hyperspectral imager, we acquired several microscenes of intimate mixtures of sand and neodymium oxide (Nd2O3). A microscene is a hyperspectral image measured in a laboratory. Several hundred thousand labeled spectra are easily and rapidly generated in one HSI cube of a microscene. Individual Petri dishes of 0, 0.5, 1, 2, 3, 4, and 5 weight-percent (wt. %) Nd2O3 with a silicate sand comprise a suite of microscenes furnishing labeled spectra for analysis. The NN and the DL/CNN both have average validation accuracies of  98 % (for the low wt. % classes); the SVM yields similar performance. As wt. % Nd2O3 increases, accuracies decrease slightly—perhaps due to the dominance of the Nd2O3 signature in the mixtures which causes an increasing difficulty in separation. For example, this could affect the 4 and 5 wt. % classes in which the Nd2O3 would be easily detected and identified with traditional, mainstay HSI algorithms. The fact that all methods can separate such low quantity classes (e.g., 0, 0.5 and 1 wt. %), though not unexpected, is encouraging and demonstrates the potential of NNs and DL/CNNs for such detailed HSI analysis.

Proceedings Title

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV

Conference Dates

April 14-19, 2019

Conference Location

Baltimore, MD, US

Conference Title

SPIE Defense and Commercial Sensing

Pub Type

Conferences

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Keywords

hyperspectral, HSI, nonlinear mixture, microscene, neural network, deep learning, DL convolutional neural network, CNN, support vector machine, SVM

Citation

Resmini, R. , Christiansen, K. and Allen, D. (2019), Unraveling Low Abundance Intimate Mixtures with Deep Learning, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, Baltimore, MD, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=927656 (Accessed October 10, 2025)