A granitic pegmatite information extraction method based on improved U-Net

LI Wanyue; LOU Debo; WANG Chenghui; LIU Huan; ZHANG Changqing; FAN Yinglin; DU Xiaochuan

doi:10.6046/zrzyyg.2022500

2024 Vol. 36, No. 2

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Article navigation > Remote Sensing for Natural Resources > 2024 > 36(2): 89-96

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LI Wanyue, LOU Debo, WANG Chenghui, LIU Huan, ZHANG Changqing, FAN Yinglin, DU Xiaochuan. 2024. A granitic pegmatite information extraction method based on improved U-Net. Remote Sensing for Natural Resources, 36(2): 89-96. doi: 10.6046/zrzyyg.2022500

Citation:

LI Wanyue, LOU Debo, WANG Chenghui, LIU Huan, ZHANG Changqing, FAN Yinglin, DU Xiaochuan. 2024. A granitic pegmatite information extraction method based on improved U-Net. Remote Sensing for Natural Resources, 36(2): 89-96. doi: 10.6046/zrzyyg.2022500

A granitic pegmatite information extraction method based on improved U-Net

1. MLR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
;2. School of Earth Science and Resource, China University of Geosciences (Beijing), Beijing 100083, China
;3. General Prospecting Institute of China National Administration of Coal Geology, Beijing 100039, China

More Information

Corresponding author: LOU Debo

Received Date: 26 December 2022

Revised Date: 28 July 2023

Publish Date: 14 June 2024

Abstract

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.
- deep learning /
- granitic pegmatite /
- U-Net /
- GF-2