Unsupervised Learning Method for Mineral Identification from Hyperspectral Data

A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization

“…clustering [8,28,29] The principle is relatively simple, easy to implement, and the convergence speed is fast. It is easy to fall into a local optimum.…”

“…However, in soft clustering, each data point can belong to more than one cluster, usually with membership associated with each cluster. Prabhavathy P et al (2019) [8] used principal component analysis to downscale the frequency bands to achieve dimensionality reduction, and then used hard and soft clustering algorithms to classify the hyperspectral data to identify minerals in the hyperspectrum. Reference [28] using KSOM for training, with clustering centers as input, enabling the system to identify six classes of minerals and to give the number of possible occurrences in each class.…”

“…Reference [29] used the K-means clustering algorithm for classification via Matlab with a known number of clusters of interest and the FCC-K-means method for unsupervised mineral identification with significantly improved performance. Reference [8] performed unsupervised training and used PCA algorithm as a band selection technique for dimensionality reduction of HSI dimensions, and hard clustering (K-means) and soft clustering (PFCM) algorithms were used to classify the given data, and the performance of PFCM was found to be better than K-means for both original HSI images and reduced bands by DBI values.…”

“…An extension of mineral identification has been greatly enhanced by the development of this technique. In recent years, most of the scholars performing intelligent mineral identification favor the model identification method; therefore, many scholars have used this method to carry out exploratory work in mineral-related geological studies, such as mineral processing [5,6], mineral prediction [4], mineral exploration mapping [7,8], chemical exploration anomaly mapping, geological mapping, core-drilling mapping [9][10][11][12], and mineral phase segmentation of X-ray microcomputer tomography data [13]. The use of artificial intelligence for intelligent mineral identification has received increasing attention and interest from researchers, and significant progress has been made.…”