收稿：2020-11-25，
纸质出版：2023-02-07
移动端阅览
针对连续池化操作丢失低层语义信息而导致建筑物提取精度低的问题，尝试以UNet++网络为基础，通过将编码器的标准卷积及最大池化替换成深度可分离卷积，以及在编码器末端利用不同采样率的空洞卷积构建多尺度空洞空间金字塔池化结构ASPP（Atrous Spatial Pyramid Pooling）来提升网络性能，并将改进后的建筑物提取网络称为残差空洞空间金字塔网络（Res_ASPP_UNet++）。为验证Res_ASPP_UNet++网络结构的有效性和适用性，以经过数据增强预处理的WHU和Massachusetts数据集作为数据源，对Res_ASPP_UNet++网络与目前常用的语义分割网络进行了试验和精度评估，并将Res_ASPP_UNet++网络与文献中的研究成果进行了对比。结果表明Res_ASPP_UNet++在模型参数量与精度两个方面均表现出优势，能够在大幅压缩模型参数量的前提下，显著提升建筑物提取精度，提取建筑物的边界更加平滑和精确，对不同尺度的建筑物表现出较强的泛化能力。
Building extraction from remote sensing imagery is an important research direction for the interpretation of remote sensing imagery. UNet++ is constructed from U-Net by connecting the decoders
resulting in densely connected skip connections
enabling dense feature propagation along skip connections and thus more flexible feature fusion at the decoder nodes. However
the traditional standard convolution in the encoder fails to fully capture the multiscale features of remote sensing imagery because of the single path for semantic feature extraction
which affects the segmentation performance of the network to some extent. To address this problem
we propose a building extraction method to improve the accuracy of building extraction from remote sensing imagery.
On the basis of UNet++
whose backbone is a deep residual network
we propose a building extraction network by replacing the standard convolution and max pooling in the encoder with depthwise separable convolution and applying an atrous spatial pyramid pooling structure (ASPP) to the end of the encoder. The network is referred to as the residual atrous spatial pyramid network (Res_ASPP_UNet++). On the basis of using dense and short connections to reduce the semantic gap between the encoder and the decoder
the Res_ASPP_UNet++ architecture uses multiscale ASPP made of several atrous convolutions with different sampling rates to sample the image in parallel to enrich semantic information by expanding the field of view and applies image-level features to encode the global context
avoiding segmentation errors caused by local features and improving the target segmentation performance.
Experiments are conducted to validate the effectiveness of the proposed methodology. We compare the frequently used semantic segmentation networks with the Res_ASPP_UNet++ architecture using the WHU and Massachusetts datasets as data sources and take intersection over union (IoU)
accuracy
precision and F1-score as evaluation indexes to evaluate the accuracy of building extraction. The experimental results are as follows: (1) The integrity of buildings extracted by Res_ASPP_UNet++ network is better than other segmentation networks on the whole
the boundary of the segmentation result is smoother and more accurate
and the result has less segmentation noise; (2) Compared with the compared semantic segmentation networks
the number of parameters of Res_ASPP_UNET ++ network is greatly compressed after improvement
the segmentation accuracy of the method is better on the whole
and it has great improvement to the UNET++ network through quantitative analysis; (3) Res_ASPP_UNET++ network has higher IoU index values than the existing building extraction algorithms listed in this paper; (4) Comparied with the proposed method without ASPP module
the value of each evaluation indexes of Res_ASPP_UNet++ with ASPP module is higher and the whole accuracy of the network with depthwise separable convolution is slightly higher than that of the network with max pooling; (5) Res_ASPP_UNet++ also extract the buildings on the Massachusetts dataset effectively and is better than other segmentation networks on the whole.
The following conclusions can be drawn from the experimental results. Multiscale ASPP and depthwise separable convolution can improve the ability to express the detailed features of the model and thus effectively improve the building extraction performance from remote sensing imagery on the basis of greatly compressing the model parameters. In addition
the Res_ASPP_UNet++ architecture is robust to buildings of different scales and types and has strong generalization ability on datasets of different resolutions and sources. In future work
we will improve the segmentation for areas with a complex distribution of buildings and overcome the missing segmentation of small buildings.
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