Journal of Geo-information Science >
High-Resolution Remote Sensing Image Building Extraction based on PRCUnet
Received date: 2021-05-21
Request revised date: 2021-07-09
Online published: 2021-12-25
Supported by
Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX20_2364)
A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions
Xuzhou Science and Technology Key R&D Program (Social Development) under Project(KC20172)
Xuzhou Science and Technology Key R&D Program(KC20172)
Open Fund of National Key Laboratory of Resource and Environment Information System
Jiangsu Province Land and Resources Science and Technology Plan Project(2021046)
Jiangsu Geology&Mineral Exploration Bureau Science and Technology Plan Project(2020KY11)
Copyright
Building extraction based on high-resolution remote sensing images has important theoretical and practical applications. Deep learning has become one of the mainstream methods for extracting buildings from high-resolution images because of its excellent deep feature extraction ability. In this paper, based on an improved structure of deep learning network, we combined the concept of minimum outer rectangle and Hausdorff distance to improve the building extraction method. The main improvements in this paper are: ① Based on the Unet network structure, we employed the multi-scale feature detection ability of Pyramid Pooling Module (PPM), the great feature extraction capability of Residual Block (RB), and the ability to balance spatial and channel information of Convolutional Block Attention Module (CBAM). The PPM, RB, and CBAM were introduced to the Unet model to build the PRCUnet model, which focuses more on semantic and detailed information and overcomes the limitation of Unet in small target detection; ② We improved the building contour optimization algorithm based on the minimum outer rectangle and Hausdorff distance to improve the generalization ability of the model. Experiments show that the accuracy, IoU, and recall of the building extraction method proposed in this paper reached above 0.85 using the test set, significantly higher than those of the Unet model. The PRCUnet model also had better extraction effect on small-scale and irregular buildings than Unet, and the optimized building contours were close to the real building boundaries.
XU Jiawei , LIU Wei , SHAN Haoyu , SHI Jiacheng , LI Erzhu , ZHANG Lianpeng , LI Xing . High-Resolution Remote Sensing Image Building Extraction based on PRCUnet[J]. Journal of Geo-information Science, 2021 , 23(10) : 1838 -1849 . DOI: 10.12082/dqxxkx.2021.210283
表1 实验环境Tab.1 Experimental environment |
CPU | GPU | Memory | System | TensorFlow | Keras | Python | CUDA | CUDNN |
---|---|---|---|---|---|---|---|---|
Intel(R) Xeon(R) CPU E5- V4 | 2×Quadro P4000 | 16GB | Ubuntu 16.04 | 2.0 | 2.1.5 | 3.7 | 9.2 | 7.6.5 |
表2 超参数设置Tab.2 Hyper parameter configuration |
Unet | Unet+ResBlock | Unet+ResBlock+PPM | PRCUnet | |
---|---|---|---|---|
迭代次数 | 61 | 63 | 63 | 58 |
学习率 | 1e-4 | 1e-4 | 1e-4 | 1e-4 |
批次大小 | 16 | 16 | 16 | 16 |
图像增强 | 是 | 是 | 是 | 是 |
表3 PRCUnet评价指标Table 3 PRCUnet Evaluation index |
模型 | Accuracy | IoU | Recall |
---|---|---|---|
Unet | 0.873 | 0.753 | 0.801 |
Unet+RB | 0.886 | 0.774 | 0.825 |
Unet+RB+PPM | 0.905 | 0.821 | 0.853 |
PRCUnet | 0.921 | 0.851 | 0.877 |
PRCUnet+轮廓优化 | - | 0.882 | - |
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