A novel 3D convolutional neural network model with supervised spectral regression for recognition of hyperspectral images of colored wool fiber

Prior knowledge of textile fiber colors in blends is useful for color recipe assessment. There have been various methods to improve the accuracy of color recognition from the dataset of colored textile fibers in recent years. However, numerical assessments based on spectral feature and color difference is insufficient, in which the accuracy of color recognition can be affected by morphology and the uneven coloration on a single fiber. This paper proposes a novel 3D convolutional neural network model (3D-CNN) with supervised spectral regression for the color recognition of hyperspectral images (HSI) of colored textile fiber. The proposed method obtained spatial-spectral features based on 3D-CNN, and the true spectrum of each class was used for supervised spectral regression to improve the accuracy. The loss function used was the sum of the supervised classification loss function and the spectral regression loss function model are optimized by mini-batch-based backpropagation. The proposed method was trained and tested on the HSI dataset composed of 100 colors of wool fibers acquired through a microscopic hyperspectral imaging system at a x3.375 optical magnification. The experimental results showed that the proposed method exhibited better performance compared to numerical assessments and other deep learning models, except for efficiency. Specifically, it achieved better recognition performance on sub-datasets of similar colored and light-colored wool fiber where subtle inter-class and large intra-class variance existed.