A method for mapping and monitoring of iron ore stopes based on hyperspectral remote sensing-ground data and a 3D deep neural network

This research explores a new hyperspectral remote sensing processing method that combines remote sensing and ground data, and builds a model based on a novel 3D convolutional neural network and fusion data. The method can monitor and map changes in iron ore stopes. First, we used an unmanned aerial vehicle-borne hyperspectral imager to take a hyperspectral image of the iron ore stope; second, collected iron ore samples and then used a ground-based spectrometer to measure the spectral data of these samples; third, combined the hyperspectral remote sensing data with the ground data and then proposed a data augmentation method. Fourth, based on the 3D convolutional neural network and deep residual network, an iron ore stope classification model is proposed. Finally, the model is applied to monitor and map iron ore stopes. The experimental results show that the proposed method is effective, and the overall accuracy is 99.62% for the five-class classification problem. The method provides a quick, accurate, and low-cost way to monitor iron ore stopes.