Spectral band selection and ANIMR-GAN for high-performance multispectral coal gangue classification

Abstract

Low-energy and efficient coal gangue sorting is crucial for environmental protection. Multispectral imaging (MSI) has emerged as a promising technology in this domain. This work addresses the challenge of low resolution and poor recognition performance in underground MSI equipment. We propose an attention-based multi-level residual network (ANIMR) within a super-resolution reconstruction model (ANIMR-GAN) inspired by CycleGAN. This model incorporates improvements to the discriminator and loss function. We trained the model on 600 coal and gangue MSI samples and validated it on an independent set of 120 samples. The ANIMR-GAN, combined with a random forest classifier, achieved a maximum accuracy of 97.78% and an average accuracy of 93.72%. Furthermore, the study identifies the 959.37 nm band as optimal for coal and gangue classification. Compared to existing super-resolution methods, ANIMR-GAN offers advantages, paving the way for intelligent and efficient coal gangue sorting, ultimately promoting advancements in sustainable mineral processing.