A granitic pegmatite information extraction method based on improved U-Net
Identifying granitic pegmatite-type lithium deposits based on remote sensing technology is a significant method for lithium ore prospecting.To enhance the information extraction accuracy of the deep learning-based semantic segmentation method for granitic pegmatites,this study improved the classic U-Net network.A batch normalization module was added to the convolutional layer of the encoder part,with the ReLU activation function replaced by the ReLU6 activation function.Simultaneously,a composite loss function was constructed to improve operational efficiency and reduce the precision loss in the training process.The domestic GF-2 images of a granitic pegmatite-type lithium deposit were employed to create a dataset for experiments.The results show that the improved U-Net model effectively identified the information on granitic pegmatites in the study area covered by GF-2 images.Compared to the original U-Net network,U-Net model based on VGG backbone network,U-Net model based on MobileNetV3 backbone network,and conventional random forest model,the improved U-Net model has its average intersection over union increased by 14.69,0.95,5.08,and 35.34 percentage points,respectively.Moreover,its F1-score increased by 18.38,1.02,5.7,and 54.59 percentage points,respectively.Hence,the improved U-Net model achieves the high-precision automatic extraction of ore-bearing granitic pegmatite information from remote sensing images in areas with low vegetation coverage.
万方数据
维普
