Lithology Identification of Clastic Rock in UAV Images Based on Data Fusion
Because of the similarity of texture of different lithology images,the accuracy of lithology identification based on a single two-dimensional image is low.In order to solve this problem,an intelligent lithology identification method based on image depth information was studied.Firstly,the depth information was fused with the image data by using four image fusion methods:channel superposition,intensity,hue,saturation(IHS)transform,wavelet transform and multi-modal fusion.Then,based on the converted image data fused with depth information,the intelligent identification method of clastic lithology was studied by using deep convolutional neural network DeepLabv3+technology.The results of intelligent lithology identification by different fusion methods and manual interpretation were compared and analyzed.The results show that different fusion methods has different lithology identification effects.In the experimental area,the lithology identification accuracy based on multi-modal fusion images is the highest,with Kappa coefficient up to 76.17％,and the overall identification accuracy up to 91.05％.The analysis shows that the intelligent lithology identification method taking into account image depth information can significantly improve the identification effect of the gravel with uneven rock surface and large height difference,but the identification effect of the mudstone and sandstone with flat surface and not obvious height difference needs to be improved.The research results provide a new technical idea for rapid lithology identification of clastic rock outcrop in a large area in the field.
data fusionlithology recognitionunmanned aerial vehicle(UAV)imageryclastic rock
万方数据
维普
