Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method

A novel hyperspectral identification method achieved high accuracy in distinguishing the state of travertine formations at Huanglong. The PSO-BPNN classifier, combining particle swarm optimization with backpropagation neural networks, demonstrated exceptional performance with an overall accuracy of 93%, F1-score of 92%, and Kappa coefficient of 97%. This method effectively categorized healthy travertine, blackened formations, those affected by algae erosion, and bare ground using hyperspectral data. The approach involved correlation analysis, sensitive band extraction, and real-world application on Micro-Hyperspectral imaging data, outperforming traditional BPNN classifiers.

Xu, M., Wang, W., Cai, J., Dai, Q., Fan, J., & Li, S. (2024). Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method. Geocarto International, 39(1). https://doi.org/10.1080/10106049.2024.2365886