Encoder-decoder structure based on conditional random field for building extraction in remote sensing images

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2021-12-07 DOI:10.4108/eai.7-12-2021.172362
Yian Xu
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Abstract

The application of building extraction involves a wide range of fields, including urban planning, land use analysis and change detection. It is difficult to determine whether each pixel is a building or not because of the large difference within the building category. Therefore, automatic building extraction from aerial images is still a challenging research topic. Although deep convolutional networks have many advantages, the networks used for image-level classification cannot be directly used for pixel-level building extraction tasks. This is caused by successive steps larger than one in the pooling or convolution layer. These operations will reduce the spatial resolution of feature maps. Therefore, the spatial resolution of the output feature map is no longer consistent with that of the input, which cannot meet the task requirements of pixel-level building extraction. In this paper, we propose a encoder-decoder structure based on conditional random field for building extraction in remote sensing images. The problem of boundary information lost by unitary potential energy in traditional conditional random field is solved through multi-scale building information. It also preserves the local structure information. The network consists of two parts: encoder sub-network and decoder sub-network. The encoder sub-network compresses the spatial resolution of the input image to complete the feature extraction. The decoder sub-network improves the spatial resolution from features and completes building extraction. Experimental results show that the proposed framework is superior to other comparison methods in terms of the accuracy on open data sets, and can extract building information in complex scenes well.
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基于条件随机场的编码器-解码器结构在遥感图像建筑物提取中的应用
建筑提取的应用涉及城市规划、土地利用分析和变化检测等广泛领域。由于建筑物类别内的差异很大,因此很难确定每个像素是否是建筑物。因此,从航拍图像中自动提取建筑物仍然是一个具有挑战性的研究课题。尽管深度卷积网络具有许多优点,但用于图像级分类的网络不能直接用于像素级建筑提取任务。这是由池化层或卷积层中连续大于1的步长造成的。这些操作会降低特征图的空间分辨率。因此,输出特征图的空间分辨率与输入特征图的空间分辨率不再一致,无法满足像素级建筑物提取的任务要求。本文提出了一种基于条件随机场的编码器-解码器结构,用于遥感图像中的建筑物提取。利用多尺度建筑信息解决了传统条件随机场中因单一势能而丢失边界信息的问题。它还保留了局部结构信息。该网络由编码器子网和解码器子网两部分组成。编码器子网对输入图像的空间分辨率进行压缩,完成特征提取。解码器子网络从特征上提高空间分辨率,完成建筑物提取。实验结果表明,该框架在开放数据集上的准确率优于其他比较方法,可以很好地提取复杂场景下的建筑信息。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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