摘要:
利用遥感手段进行花岗伟晶岩型锂矿的识别是锂矿找矿勘查中的重要方法之一。为提高深度学习语义分割方法在花岗伟晶岩这一特殊场景中的信息提取精度,文章对经典U-Net网络进行了改进。在编码部分卷积单元层中加入批量归一化模块,使用ReLU6激活函数代替ReLU激活函数,同时构建复合损失函数,以提高运算效率,减少训练过程中的精度损失。使用国产GF-2花岗伟晶岩型锂矿影像制作数据集进行实验,结果表明,改进U-Net模型对GF-2影像研究区内花岗伟晶岩信息的识别效果较好,相比原始U-Net网络、基于VGG主干网络的U-Net模型、基于MobileNetV3主干网络的U-Net模型以及传统随机森林模型,平均交并比分别提高了14.69,0.95,5.08和35.34百分点,F1-score分别提高了18.38,1.02,5.7和54.59百分点,实现了低植被覆盖区域遥感影像中含矿花岗伟晶岩信息的高精度自动化提取。
Abstract:
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.
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