Unraveling low abundance intimate mixtures with deep learning

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 neural network 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.