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Deep convolutional neural networks have strong data structure mining ability and can be successfully applied to facilitate the feature detection process when applied to porous media analysis. One of the encountered limitations is the lack of large data when applying deep learning algorithms. This work proposes a novel approach to overcome this problem by generating highly representative datasets based on real CT-images. The method uses an original morphological concept for the determination of pore size distribution to provide input data that is to be fed to a designed neural network. The generated data was employed for the classification of soil aggregates that differ in their pore size distribution. The image classification results were achieved by exploiting well-known pre-trained deep learning models: VGG-16, ResNet50, InceptionV3, Dense-Net121, and MobileNet. We applied k-fold cross-validation for k equal to five to validate the results. The average accuracy for the validating data achieved for the MobileNet, VGG-16, InceptionV3 and DenseNet121 ranged from 90% to 95% and were 5% higher compared to ResNet50. The deep learning approach has demonstrated great promise for analyzing very complex and irregular structures based on micro-CT images.
