顾及区域信息的卷积神经网络在影像语义分割中的应用
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武汉大学电子信息学院

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TP753

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Considering the Regional Information of Convolutional Neural Network in the Application of the Image Semantic Segmentation
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School of electronic information, Wuhan University

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    摘要:

    高分辨率遥感影像在实际应用中得到广泛使用,高分影像语义分割方法的研究具有重要实际应用价值。近来基于深度卷积网络的遥感影像标注方法表现出了比传统方法更为优越的性能,然而由于其基于固定感受野大小的上下文信息获取方法没有显式利用像素间约束关系,导致同一地物内部语义标注结果不一致。基于同一区域内部像素属于相同类别概率较大的假定,本文试图引入图像区域内部语义标注一致性约束以改善现有深度卷积神经网络描述上下文信息的能力。在现有全卷积网络模型基础上,利用卷积神经网络最后一层特征引入一个表示区域内部像素特征一致性的损失函数,将该损失函数与softmax损失函数进行联合训练得到网络模型参数。在ISPRS(国际摄影测量与遥感学会)的Vaihingen 2D语义标注数据集上对本文提出的方法进行了实验验证,实验结果表明所提方法在大多数类别上取得了较现有卷积神经网络模型更优的分类结果,总体准确率达85.18%。本文提出的引入区域内部像素标记一致性的全卷积网络模型,可以有效捕捉区域内部像素特征一致性的上下文信息,能有效纠正全卷积网络模型在区域内部像素分类中的冲突,获得区域一致较好的分类结果,从而改善图像的语义标注效果。

    Abstract:

    High resolution remote sensing image have been widely used in practical applications, the research of high resolution image semantic segment method has important practical application value. Recently, the remote sensing image annotation method based on the deep convolution network has shown better performance than the traditional methods. However, the context information acquisition method based on the fixed receptive field size does not explicitly use the inter pixel constraint, lead to inconsistent semantic annotation results in the same object internal. Based on the assumption that the pixels in the same region have lager probability to belong to the same category, this paper attempts to introduce a consistency constraint of semantic annotation within the image region to improve the existing depth convolution neural network's ability to describe the context information. On the basis of the existing fully convolutional network model, a loss function representing the consistency of pixel features in the same region is introduced by using the last layer feature of the CNN, combine this loss function with softmax loss function to obtain the network model parameters by joint training. The proposed method is validated on the Vaihingen 2D semantic annotation dataset of ISPRS (International Society for Photogrammetry and remote sensing). The experimental results show that the proposed method achieves better classification results than the existing convolutional neural network models in most categories, and the overall accuracy rate is 85.18%. The fully convolutional network model of regional internal pixel labeling consistency proposed in this paper can effectively capture the context information of the pixel consistency in a region, rectify the classification conflict in traditional FCNs, and get a better consistent classification result, thus improve the image annotation effect.

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伍佳. 顾及区域信息的卷积神经网络在影像语义分割中的应用[J]. 科学技术与工程, 2018, 18(21): .
Wu Jia. Considering the Regional Information of Convolutional Neural Network in the Application of the Image Semantic Segmentation[J]. Science Technology and Engineering,2018,18(21).

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  • 收稿日期:2017-08-31
  • 最后修改日期:2017-11-29
  • 录用日期:2017-12-05
  • 在线发布日期: 2018-07-30
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