Affiliations: [a] Department of Computer Science and Engineering, VSSUT Burla, India
| [b] Department of Electronics and Communication Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India
| [c] Department of Electronics and Communication Engineering, SUIIT, Sambalpur University, Odisha, India
| [d] Department of Electronics, Sambalpur University, Odisha, India
| [e] Department of Electronics and Communication Engineering, Guru Ghasidas Vishwavidyalaya, Bilapur, C.G., India
Abstract: Mineral classification is a crucial task for geologists. Minerals are identified by their characteristics. In the field, geologists can identify minerals by examining lustre, color, streak, hardness, crystal habit, cleavage, fracture, and specific features. Geologists sometimes use a magnifying hand lens to identify minerals in the field. Surface color can assist in identifying minerals. However, it varies widely, even within a single mineral family. Some minerals predominantly show a single color. So, identifying minerals is possible considering surface color and texture. But, again, a limited database of minerals is available with large-scale images. So, the challenges arise to identify the minerals using their images with limited images. With the advancement of machine learning, the deep learning approach with bi-layer feature fusion enhances the dimension of the feature vector with the possibility of high accuracy. Here, an experimental analysis is reported with three possibilities of bi-layer feature fusion of three CNN models like Alexnet, VGG16 & VGG19, and a framework is suggested. Alexnet delivers the highest performance with the bi-layer fusion of fc6 and fc7. The achieved accuracy is 84.23%, sensitivity 84.23%, specificity 97.37%, precision 84.7%, FPR 2.63%, F1 Score 84.17%, MCC 81.75%, and Kappa 53.59%.
Keywords: Mineral identification, deep learning, bi-layer feature fusion, deep feature
