@Article{2019.100000084,
AUTHOR = {Yani Hou, Wenzhong Zhu, Erli Wang},
TITLE = {Hyperspectral Mineral Target Detection Based on Density Peak},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {4},
PAGES = {805--814},
URL = {http://www.techscience.com/iasc/v25n4/39710},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000084}
}