Learning Method for In-Situ Soil Classification Based on Texture Characteristics

Knowing the nature of a soil through its classification is a key step in understanding its behaviour. In addition to its mechanical properties, information on soil type is important for forecasting material behaviour. This requires laboratory tests that are often long and expensive. Our approach is a development of in-situ identification tests used to complement “blind” mechanical tests. A new method is proposed for obtaining in-situ soil classification based on the use of geo-endoscopy and image analysis. This technique is based on the computation of texture features in geoendoscopic images and, more particularly, second order statistical features. After a presentation of the goals of this research, this article presents the methodology proposed and the learning steps performed to achieve these goals. The second part describes the testing and selection of major textural features according to their performances and their discriminating capacities. In the last part the classification results obtained with this methodology are presented. Soil image classification has been tested successfully and achieves accuracy of over 80 % in classifying natural soil samples. Because of the fixed magnification of the camera, results presented here are strongly linked to this magnification.