Hyperspectral Mineral Target Detection Based on Density Peak

Hyperspectral remote sensing, with its narrow band imaging, provides the potential for fine identification of ground objects, and has unique advantages in mineral detection. However, the image is nonlinear and the pure pixel is scarce, so using standard spectrum detection will lead to an increase of the number of false alarm and missed detection. The density peak algorithm performs well in high-dimensional space and data clustering with irregular category shape. This paper used the density peak clustering to determine the cluster centers of various categories of images, and took it as the target spectrum, and took the clustering results as the ground data. Two methods of HUD and OSP were used to detect the image, and the correlation coefficients of the spectrum of each cluster center and the mineral spectrum of the spectral library were obtained. Finally, the results were compared with the mapping results of Clark et al. The experimental results showed that the cluster center spectrum as the target can well detected the distribution of the corresponding minerals, and it has higher correlation coefficient with mineral in the result of mapping.