Rapid multispectral image identification of coal and gangue based on super-resolution reconstruction

Accurate coal and gangue separation is crucial for efficient coal utilization. Multispectral imaging (MSI) offers a promising approach but often suffers from limited resolution, hindering accurate identification. This study proposes, a novel method, to our knowledge, combining super-resolution (SR) reconstruction and machine learning to enhance coal and gangue identification in MSI. A spectral attention mechanism and an enhanced multi-scale residual network with GAN (SAM-EMSR-GAN) were developed and evaluated alongside four established SR methods: SRCNN, VDSR, ESRGAN, and DRMSFFN. MSI images of 300 coal and 300 gangue samples were reconstructed, using each method to compare their performance. SAM-EMSR-GAN achieved superior reconstruction, attaining the highest structural similarity index (SSIM) of 0.906 and peak signal-to-noise ratio (PSNR) of 32.97 at 4× magnification. The study further investigated the combination of the SR method with seven widely used classification models: CatBoost, random forest (RF), support vector machine (SVM), least squares support vector machines (LSSVMs), eXtreme gradient boosting (XGBoost), ResNet50, and ResNet101. CatBoost consistently delivered the highest classification accuracy across all SR methods, reaching 97.32% accuracy at 959.37 nm when paired with SAM-EMSR-GAN. Independent validation using a separate dataset confirmed the robustness of this approach, achieving a 92.49% accuracy. These findings demonstrate the potential of combining SAM-EMSR-GAN and CatBoost for accurate and efficient coal and gangue identification, paving the way for intelligent and automated coal sorting technologies.