Open Access
ARTICLE
Hyperspectral Mineral Target Detection Based on Density Peak
Yani Hou, Wenzhong Zhu, Erli Wang
School of Computer Science, Sichuan University of Science & Engineering, Zigong, Sichuan, 643000, China
* Corresponding Author: Yani Hou,
Intelligent Automation & Soft Computing 2019, 25(4), 805-814. https://doi.org/10.31209/2019.100000084
Abstract
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.
Keywords
Cite This Article
Y. Hou, W. Zhu and E. Wang, "Hyperspectral mineral target detection based on density peak,"
Intelligent Automation & Soft Computing, vol. 25, no.4, pp. 805–814, 2019.