A strongly supervised hyperspectral unmixing framework for precise mineral composition and coal ash content estimation

Accurate coal ash content detection is essential for advancing intelligent clean coal processing and holds significant practical value across mining, washing, combustion, and conversion technologies. This paper introduces a strongly supervised hyperspectral unmixing (SSHU) framework designed to estimate mineral composition proportions and ash content. We conducted systematic ablation experiments on concentrated coal and tailings coal datasets to evaluate the method's effectiveness and analyze the mechanisms of proportional prior information and reconstruction decoders. Results demonstrate that proportional prior information effectively constrains the proportional encoder, making estimated mineral and pure coal distributions closer to actual material distributions. The reconstruction decoder enhances the proportional encoder's feature extraction ability, guides model convergence, and improves both proportion and ash content estimation accuracy. Compared to existing hyperspectral unmixing methods, our approach incorporates pure substance spectral information during model training and combines proportional prior constraints. This provides a robust solution for complex mixture analysis and demonstrates significant potential in hyperspectral unmixing applications.