Rock thin section image classification based on depth residuals shrinkage network and attention mechanism

In the geological field, recognizing rock thin section images under a microscope is of great significance to geological research and mineral resources exploration. Compared with other images, the rock thin section microscope image has complex features and rich information. When classifying for them, the redundant information and features that are useless for classification tasks will affect the model classification effect. Thus this paper proposes a rock image classification algorithm based on depth residuals shrinkage network and attention mechanism to suppress the useless information. The subnetwork of obtaining threshold value is improved, and the global maximum pooling of features is added as information representation, and the soft threshold function is improved by adding the weight coefficient which is based on attention mechanism to distinguish the importance of different features. Moreover, three rock thin section microscopic image classification algorithms fusing multidimensional information are designed, and the orthogonal polarized image and single polarized image of rock thin section are input as the base data, which make full use of the multidimensional information of rock thin section microscopic image. This study used 20,242 images of 12 kinds of rock thin sections as samples to train and verify the above method. The results show that the method proposed in this paper can effectively improve the recognition accuracy of rock thin section images under a microscope.