高分辨率遥感影像建筑物提取多路径RSU网络法

doi:10.11947/j.AGCS.2021.20200508

摘要/Abstract

摘要： 针对卷积神经网络在提取建筑物的过程中，存在建筑物边界不准确和建筑物内部空洞等问题，提出以RSU模块（residual U-block）为核心的MPRSU-Net （multi-path residual U-block network）。该模块利用编码器-解码器结构和残差连接，实现了局部特征和多尺度特征的融合。由于一个RSU模块提取的信息有限，MPRSU-Net进一步通过多路径结构并行了不同尺度的RSU模块，并在这些模块之间进行信息交换，提高了特征聚集效率。在分辨率为0.3 m的WHU和Inria建筑物数据集上进行试验，精度分别达95.65%和88.63%，IoU分别达91.17%和79.31%，验证了本文方法的有效性。此外，本文方法相较于U²Net，计算量明显降低，模型参数量减少68.63%，表明本文方法具有一定的应用价值。

关键词: 高分辨率遥感影像, 建筑物提取, 多尺度, 卷积神经网络, 多路径

Abstract: Inaccurate boundaries and holes are two major problems when extracting buildings from high-resolution remote sensing images by a convolution network. To solve these problems, we proposed the MPRSU-Net (multi-path residual U-block network), which is based on the RSU (residual U-block). The RSU is able to fuse local features and multi-scale features, with the help of the encoder-decoder structure and the residual connection. However, a single RSU is not enough to gather enough information, MPRSU-Net parallels RSU blocks of different scales by the multi-path structure and exchanges information among these blocks to further enhance the feature aggregation efficiency. Experimental results showed that the MPRSU-Net achieved 95.65%,88.63% precision, and 91.17%,79.31% IoU on 0.3 m resolution WHU and Inria building datasets, which showed the effectiveness of the proposed method. In addition, compared with the U² Net, MPRSU-Net is much lighter in computation and reduces the amount of model parameters by 68.63%, demonstrating that the method has some application value.

Key words: high-resolution remote sensing image, building extraction, multi-scale, convolutional neural networks, multi-path

中图分类号:

P237

张玉鑫, 颜青松, 邓非. 高分辨率遥感影像建筑物提取多路径RSU网络法[J]. 测绘学报, 2022, 51(1): 135-144.

ZHANG Yuxin, YAN Qingsong, DENG Fei. Multi-path RSU network method for high-resolution remote sensing image building extraction[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(1): 135-144.

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