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MAPDNet：一种检测XCO2图像中羽流的方法
作者:
作者单位:

南京信息工程大学

基金项目:

国家自然科学基金项目（面上项目，重点项目，重大项目）


MAPDNet - a method for detecting plumes in XCO2 images
Author:
Affiliation:

Nanjing University of Information Science and Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    检测温室气体羽流是碳排放监测的关键任务，目前相关算法中，对羽流边缘细节的分割精度还有提升空间。为此，本文提出一种基于多尺度注意力机制的羽流检测网络（MAPDNet）。首先，本文提出了多尺度池化条带卷积模块（MPSCM），补偿编码器降采样操作中稀释的高级语义信息，增强主要的羽流轮廓信息，减少细节信息的丢失。然后，关系注意力模块（RAM）建立了特征之间的全局信息与局部信息交互，增强有用特征，降低特征图中冗余信息的比例，提高模型的分割精度。此外，本文还提出了上下文特征融合模块（CFFM）以便网络更好地理解上下文特征的信息，在恢复图像分辨率的同时，较好地将特征对齐，恢复空间位置信息。最后，边界增强模块（BEM）实现端到端训练，增强输出分割边界的细节，进一步提高分割精度。实验结果表明，MAPDNet模型能够从XCO2羽流图像中检测碳羽流，并在模拟羽流数据集上具有良好的分割性能，其分割精度优于现有方法。

    Abstract:

    The detection of greenhouse gas plumes is a key task of carbon emission monitoring. In the current relevant algorithms, the segmentation accuracy of plume edge details still has room for improvement. In this paper, a multi-scale attention mechanism based plume detection network (MAPDNet) is proposed. Firstly, a multi-scale pooled strip Convolution module (MPSCM) is proposed to compensate the high-level semantic information diluted in the downsampling operation of the encoder, enhance the main plume profile information, and reduce the loss of detail information. Then, the relational attention module (RAM) establishes the global and local information interaction between features, enhances the useful features, reduces the proportion of redundant information in the feature map, and improves the segmentation accuracy of the model. In addition, a context feature fusion module (CFFM) is proposed in this paper so that the network can understand the context feature information better, align the features better, and recover the spatial position information while recovering the image resolution. Finally, the edge enhancement module (BEM) implements end-to-end training, enhances the details of the output segmentation boundary, and further improves the segmentation accuracy. The experimental results show that MAPDNet model can detect carbon plumes from XCO2 plume images, and has good segmentation performance on simulated plume data sets, and its segmentation accuracy is better than the existing methods.

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邵鹏飞,胡凯,刘滋然,冯新龑,马科宇,章琪,姜闪闪,夏旻,叶小岭. MAPDNet：一种检测XCO2图像中羽流的方法[J].南京信息工程大学学报,,():

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  • 收稿日期:2024-06-28
  • 最后修改日期:2024-09-27
  • 录用日期:2024-09-28

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