Multivariate Image Regression for Quality Control of Natural Fiber Composites

Continuum removal for ground-based LWIR hyperspectral infrared imagery applying non-negative matrix factorization

Continuum removal is vital in hyperspectral image analysis. It enables data to be used for any application and usually requires approximations or assumptions to be made. One of these approximations is related to the calculation of the spectra of the background's blackbody temperature. Here, we present a new method to calculate the continuum removal process. The proposed method eliminates the calculation for ground-based hyperspectral infrared imagery by applying two acquisition sets before and after using the heating source. The approach involves a laboratory experiment on a long-wave infrared (LWIR; 7.7-11.8 μm), with a LWIR-macro lens, an Infragold plate, and a heating source. To calculate the continuum removal process, the approach applies non-negative matrix factorization (NMF) to extract Rank-1 NMF, estimate the downwelling radiance, and compare it with that of other conventional methods. NMF uses gradient-descent-based rules (GD) and non-negative least-squares (NNLS) optimization algorithms to obtain Rank-1 NMF. A comparative analysis is performed with 1%-20% additive noise for all algorithms by using the spectral angle mapper and normalized cross correlation (NCC). Results reveal the promising performance of NMF-GD (average of 72.5% similarity percentage using NCC) and NMF-NNLS (average of 77.6% similarity percentage using NCC).