Multilayer Convolutional Parameter Tuning based Classification for Geological Igneous Rocks

A framework different CNN has been proposed to solve image classification. The power of CNN and the ability to extract demanding features has to be a target for proposed the new ideas. In the geology domain, issues in ascertaining igneous rock from volcanic eruptions often contrast in classification when explored from the location of the rocks. These domain problems must be resolved, contemplating to have consistency and accelerate rock classification. CNN has used to figure out the problem by expanding in multilayer convolution. Besides, parameter tuning has anointed to get the high accuracy to enhance the CNN model. This study has exploited many parameters tuning such as rescaling, cropping, size of inaccurate filter prediction. The exploration has shown that CNN(64,5) achieves a high accuracy of 98.9% and validation carries out accuracy of 81.1%. This study has confirmed that enumerating the tuning parameter on rescaling and cropping does not boost accuracy, even modifying the filter size and stride. Some results have shown still have an inaccuracy class, specifically in the diorite and limestone. The error forecast is 31.7% of 41 predicted diorite images and 30% of 50 predicted limestone images, respectively. (Abstract)