A Spatial-Spectral Component Representation Network for Fine-Grained Mineral Classification in Hyperspectral Imagery

Mineral mapping is a significant focus in remote sensing research, which aims to fully utilize spectral data for identifying and describing the composition of surface minerals. To gain a deeper understanding of mineral distribution within the study area, fine-grained hyperspectral image classification (HSIC) has emerged as an important research challenge. In mineral scenes where mineral co-occurrence and severe spectral mixing are prevalent, variations in mineral abundances largely govern discriminability, making abundance information essential for improving the reliability of mineral identification. Motivated by these considerations, we develop a spatial-spectral component representation network for HSIC, called SSCRNet, which introduces an Autoencoder (AE)-structured unmixing network to achieve complementary integration between abundance features and deep features extracted by the network. The SSCRNet enhances deep classification networks by introducing convolutional layers with different dilation rates, which enables the simultaneous capture of fine-grained mineral texture details and large-scale geological structural patterns in mineral areas. This design effectively improves the classification performance for complex surface types. Experimental results demonstrate that SSCRNet outperforms existing state-of-the-art approaches in classification performance and robustness for mineral mapping scenarios. The code and datasets will be available athttps://github.com/ZhaoYGeo/SSCRNet.