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多光谱影像引导的遥感影像道路提取网络

MSNet 최신버젼 다운로드

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# MSNet

##多光谱影像引导的遥感影像道路提取网络

环境的一些关键配置：

=============================================

numpy                     1.21.5                   pypi_0    pypi
    opencv-python             4.6.0.66                 pypi_0    pypi
    pip                       22.3                     pypi_0    pypi
    python                    3.9.12               h6244533_0
    scikit-image              0.19.3                   pypi_0    pypi
    scikit-learn              1.1.1                    pypi_0    pypi
    scipy                     1.8.1                    pypi_0    pypi
    seaborn                   0.12.0                   pypi_0    pypi
    stack-data                0.6.0                    pypi_0    pypi
    tensorboard               2.10.0                   pypi_0    pypi
    tensorboardx              2.5.1                    pypi_0    pypi
    tinycss2                  1.2.1                    pypi_0    pypi
    torch                     1.12.0+cu116             pypi_0    pypi
    torchvision               0.13.0+cu116             pypi_0    pypi
    tqdm                      4.64.0                   pypi_0    pypi
    vc                        14.2                 h21ff451_1
    visdom                    0.1.8.9                  pypi_0    pypi
    vs2015_runtime            14.27.29016          h5e58377_2
    zipp                      3.8.1                    pypi_0    pypi
=============================================

图像输入保持4倍关系，如：融合图像400 X 400，多光谱影像100 X 100
 
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 GF2-FC数据获取地址：
 
 https://pan.baidu.com/s/1Xs_-SpkuXUzjaRWOoTbT_g?pwd=jwez 提取码: jwez (论文中的实验数据，附已训练好的权重文件)
 
 完整的全色、多光谱和融合实验数据链接（提供了遥感数据地理坐标，并对数据进行了更新）：
 
 https://drive.google.com/drive/folders/13IHhLjQbDism4jmAwiLOs52roiOK2OUT?usp=drive_link
 
 =============================================
 
 请下载完毕后，删除test_data和train_data中所有的readme文件进行训练/测试
 
 =============================================
 
 引用格式：Y. Du, Q. Sheng, W. Zhang, C. Zhu, J. Li, B. Wang, “From local context-aware to non-local: A road extraction network via guidance of multi-spectral image,” ISPRS J. 
 Photogramm. Remote Sens., vol. 203, pp. 230-245, Sep. 2023, https://doi.org/10.1016/j.isprsjprs.2023.07.026
 
 =============================================

MSNet

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