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基于CU-Net模型的红树林自动识别方法研究

蔚铭阁 芮小平 邹亚荣 张茜

蔚铭阁,芮小平,邹亚荣,等. 基于CU-Net模型的红树林自动识别方法研究−以广东省珠海市淇澳岛为例[J]. 海洋学报,2023,45(3):125–135 doi: 10.12284/hyxb2023054
引用本文: 蔚铭阁,芮小平,邹亚荣,等. 基于CU-Net模型的红树林自动识别方法研究−以广东省珠海市淇澳岛为例[J]. 海洋学报,2023,45(3):125–135 doi: 10.12284/hyxb2023054
Yu Mingge,Rui Xiaoping,Zou Yarong, et al. Research on automatic identification method of mangrove based on CU-Net model: Taking the Qi’ao Island in Zhuhai City, Guangdong Province as an example[J]. Haiyang Xuebao,2023, 45(3):125–135 doi: 10.12284/hyxb2023054
Citation: Yu Mingge,Rui Xiaoping,Zou Yarong, et al. Research on automatic identification method of mangrove based on CU-Net model: Taking the Qi’ao Island in Zhuhai City, Guangdong Province as an example[J]. Haiyang Xuebao,2023, 45(3):125–135 doi: 10.12284/hyxb2023054

基于CU-Net模型的红树林自动识别方法研究以广东省珠海市淇澳岛为例

doi: 10.12284/hyxb2023054
基金项目: 海南省重点研发计划(高新技术专项);国家自然科学基金(41771478)。
详细信息
    作者简介:

    蔚铭阁(1998-),女,河南省汝州市人,研究方向为深度学习和深度迁移学习识别红树林。E-mail:yumg@hhu.edu.cn

    通讯作者:

    芮小平,教授,主要从事地理信息系统理论与应用方面的研究。E-mail:ruixp@hhu.edu.cn

    张茜,副研究员,主要从事海洋遥感方面的研究。E-mail:zhangxi@mail.nsoas.org.cn

  • ① a图审图号为GS(2019)3266号。
  • 中图分类号: TP79;S718.5

Research on automatic identification method of mangrove based on CU-Net model: Taking the Qi’ao Island in Zhuhai City, Guangdong Province as an example

  • 摘要: 红树林对维护生物多样性以及生态平衡等具有重要意义。因此,高效、精确地提取红树林植被信息以及实时对其进行监测十分必要。本文提出了一种高分辨率遥感影像红树林像素级精确提取的深度学习方法。针对红树林遥感分类精度不高的问题,通过强化图像中心信息,弱化边缘信息的方法引入CLoss损失函数,添加Dropout、Batch Normalization层构建了适用于红树林识别的CU-Net模型,采用滑动重叠拼接方法构建了新的预测模型,有效解决了预测结果边缘信息不足以及有拼接痕迹的问题。将本文方法的识别结果与U-Net、SegNet、DenseNet模型的预测结果以及传统的SVM、RF方法进行对比,结果表明,本文模型相较于其他深度学习模型泛化能力更强,识别效果更好,在两个测试区域的平均总体精度、平均交并比分别达到了94.43%、88.12%,平均F1-分数在红树林和普通树木的精度分别达到了95.96%、90.49%,精度明显高于传统的SVM、RF方法,也高于其他几种神经网络方法,验证了该模型在红树林识别领域的有效性,可为高分辨率遥感红树林识别领域提供一条新的思路。
    1)  ① a图审图号为GS(2019)3266号。
  • 图  1  研究区地理位置及区域遥感影像

    Fig.  1  Geographic location of the study area and regional remote-sensing images

    图  2  滑动窗口裁剪示意图

    Fig.  2  Schematic diagram of overlapping sliding window cropping

    图  3  融合后的 GF-1 样本集影像和标签图

    Fig.  3  GF-1 sample set of fused images and their corresponding labels

    图  4  本文方法流程图

    Fig.  4  Flow chart of the method applied in this paper

    图  5  CU-Net模型结构

    Fig.  5  CU-Net model structure

    图  6  CLoss 计算区域示意图

    Fig.  6  CLoss calculation area diagram

    图  7  输入图像的10 个卷积层特征映射图

    Fig.  7  Feature-mapping maps of 10 convolutional layers of the input image

    图  8  预测方法示意图

    Fig.  8  Diagram of prediction method

    图  9  训练集和验证集的准确率和损失值

    Fig.  9  Accuracy and loss graphs for the training and validation datasets

    图  10  测试区红树林识别结果对比图

    Fig.  10  Comparison of mangrove identification results in the test area

    图  11  测试区域6种方法的差分图像

    Fig.  11  Difference images for the six methods in the test area

    表  1  GF-1卫星影像参数

    Tab.  1  GF-1 satellite image parameters

    载荷谱段号谱段范围/μm空间分辨率/m幅宽/km
    全色相机10.45~0.90260
    多光谱相机20.45~0.52860
    30.52~0.59860
    40.63~0.69860
    50.77~0.89860
    下载: 导出CSV

    表  2  网络训练参数设置

    Tab.  2  Network training parameters setting

    参数具体设置
    批处理大小16
    学习率0.000 1
    训练次数300
    优化器Adam
    下载: 导出CSV

    表  3  测试区域1的精度评价结果

    Tab.  3  Test area 1 precision evaluation results

    测试区CU-NetU-NetSegNetDenseNetSVMRF
    红树林其他树木红树林其他树木红树林其他树木红树林其他树木红树林其他树木红树林其他树木
    总体精度/%94.0091.5492.0188.3379.8073.91
    平均交并比/%87.2183.3583.3676.1865.3160.59
    F1-分数/%95.7889.1693.3984.2994.4285.2691.1372.0983.4654.8175.4554.60
    下载: 导出CSV

    表  4  测试区域2的精度评价结果

    Tab.  4  Test area 2 precision evaluation results

    测试区CU-NetU-NetSegNetDenseNetSVMRF
    红树林其他树木红树林其他树木红树林其他树木红树林其他树木红树林其他树木红树林其他树木
    总体精度/%94.8691.2892.5986.6764.3872.69
    平均交并比/%89.0383.7784.7475.0351.6057.30
    F1-分数/%96.1491.8291.0588.6192.1592.3286.6874.8546.9955.3348.6271.86
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-07
  • 修回日期:  2022-10-26
  • 网络出版日期:  2022-10-31
  • 刊出日期:  2023-02-01

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