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面向滨海生态监管的多尺度目标语义分割研究

Multi-scale Objectives Semantic Segmentation for Coastal Ecological Supervision

Environment and Resource / 2022,4(2): 48-61 / 2022-07-13 539 918

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• Authors: 陈岩¹ 杨晓彤² 奚砚涛³ 徐立祥¹ 李新路¹
• Information:

  1．合肥学院人工智能与大数据学院，合肥；
  2．国家海洋信息中心海洋测绘地理信息部，天津；
  3．中国矿业大学资源与地球科学学院，徐州

• Keywords:
  Coastal ecological supervision; Artificial intelligence; Mangroves; Mariculture; Benchmark dataset; Multi-scale feature fusion; Semantic segmentation
  滨海生态监管; 人工智能; 红树林; 海水养殖; 基准数据集; 多尺度特征融合; 语义分割
• Abstract: To improve the lack of deep learning dataset of coastal ecological scenes and low accuracy of multi-scale objectives semantic segmentation for remote sensing image classification, we take three types of coastal typical ecological supervision multi-scale objectives of mangrove, raft cultivation and pond aquaculture as research objects, constructs a benchmark dataset for coastal ecological supervision, improves the UNet feature fusion by integrating batch normalization and spatial dropout modules, and proposes a multi-scale deep convolutional semantic segmentation model. The model has an overall accuracy of 92% on the test set, a kappa coefficient of 0.87, and a mIoU of 82%. The experimental results show that the coupled stacking of batch normalization and feature fusion spatial dropout can effectively suppress multiscale objectives semantic segmentation overfitting and improve the model accuracy and generalization performance. The proposed model and the constructed semantic segmentation dataset for coastal ecological supervision can provide decision support for ecological restoration, mapping and comprehensive management in coastal areas. 针对缺少滨海生态场景深度学习数据集，面向遥感影像分类的多尺度目标语义分割精度不高等问题，研究以红树林、浮筏养殖和围塘养殖三类滨海典型生态监管多尺度目标为研究对象，构建了面向滨海生态监管的多目标语义分割数据集，通过集成批归一化和空间置弃算法，改进UNet 特征融合策略，提出了一种多尺 度深度卷积语义分割模型。模型在测试集上总体精度92%，Kappa 系数0.87，平均交并比82%。实验结果表明批归一化与特征融合空间置弃的耦合堆叠可有效抑制多尺度目标语义分割过拟合，提高模型精度和泛化性能。研究提出的模型及构建的面向滨海生态环境监管的多目标语义分割数据集可为滨海区域生态修复、测绘和综合治理提供决策支持。
• DOI: https://doi.org/10.35534/er.0402007
• Cite: 陈岩，杨晓彤，奚砚涛，等．面向滨海生态监管的多尺度目标语义分割研究［J］．环境与资源，2022，4（2）：48-61．