New Identification of Sericite Subclass Minerals Using Airborne Hyperspectral Data in the Xitan Region of Gansu Province and its Significance in Gold Ore Prospecting

Objective Hyperspectral remote sensing has attracted much attention in remote sensing research during recent years. It can elaborately identiry spectral characteristics of different objects by acquiring continuous spectral curves of ground objects, and can thus provide more information for geological research （Zhao Yingjun et al., 2015）. With the deepening hyperspectral remote sensing research, scholars have focused from the classification of alteration minerals to the identification of subclass minerals in order to explore their significance fbr ore prospecting. This work utilized hyperspectral remote sensing technology in the Xitan region of Gansu Province to identify limonite and two types of sericite subclass minerals, and conducted field verification and geochemical survey. In addition, we analyzed the geological environment of subclass sericite minerals （Van Ruitenbeek et al., 2006） to provide evidence for gold ore prospecting.

Spectral clustering eigenvector selection of hyperspectral image based on the coincidence degree of data distribution

Spectral clustering is a well-regarded subspace clustering algorithm that exhibits outstanding performance in hyperspectral image classification through eigenvalue decomposition of the Laplacian matrix.However,its classification accuracy is severely limited by the selected eigenvectors,and the commonly used eigenvectors not only fail to guarantee the inclusion of detailed discriminative information,but also have high computational complexity.To address these challenges,we proposed an intuitive eigenvector selection method based on the coincidence degree of data distribution(CDES).First,the clustering result of improved k-means,which can well reflect the spatial distribution of various types was used as the reference map.Then,the adjusted Rand index and adjusted mutual information were calculated to assess the data distribution consistency between each eigenvector and the reference map.Finally,the eigenvectors with high coincidence degrees were selected for clustering.A case study on hyperspectral mineral mapping demonstrated that the mapping accuracies of CDES are approximately 56.3%,15.5%,and 10.5%higher than those of the commonly used top,high entropy,and high relevance eigenvectors,and CDES can save more than 99%of the eigenvector selection time.Especially,due to the unsupervised nature of k-means,CDES provides a novel solution for autonomous feature selection of hyperspectral images.