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收稿：2019-10-22，
修回：2020-2-19，
录用：2020-2-26，
纸质出版：2020-12-16
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目的
2
城镇建成区是城镇研究重要的基础信息，也是实施区域规划、落实城镇功能空间布局的前提。但是遥感影像中城镇建成区的环境复杂，同时不同城镇建成区在坐落位置、发展规模等方面存在许多差异，导致其信息提取存在一定困难。
方法
2
本文基于面向图像语义分割的深度卷积神经网络，使用针对特征图的强化模块和通道域的注意力模块，对原始DeepLab网络进行改进，并通过滑动窗口预测、全连接条件随机场处理方法，更准确地实现城镇建成区提取。同时，针对使用深度学习算法容易出现过拟合和鲁棒性不强的问题，采用数据扩充增强技术进一步提升模型能力。
结果
2
实验数据是三亚和海口部分地区的高分二号遥感影像。结果表明，本文方法的正确率高于93%，Kappa系数大于0.837，可以有效地提取出大尺度高分辨率遥感影像中的城镇建成区，且提取结果最为接近实际情况。
结论
2
针对高分辨率遥感卫星影像中城镇建成区的光谱信息多样化、纹理结构复杂化等特点，本文算法能在特征提取网络中获取更多特征信息。本文使用改进的深度学习方法，提出两种处理方法，显著提高了模型的精度，在实际大幅遥感影像的使用中表现优秀，具有重要的实用价值和广阔的应用前景。
Objective
2
The urban built-up area is an important source of basic information for urban research and serves as a prerequisite for the regional planning and implementation of the spatial layout of urban functions. Given the recent developments in Earth observational technologies and improvements in the resolution of remote sensing images
accurately and efficiently extracting information on urban built-up areas has become possible. However
due to the complex environment of urban built-up areas in high-resolution remote sensing images and the variations in their locations and development scales
various forms of remote sensing image representations increase the difficulty of using traditional information extraction methods for urban built-up areas. Recent studies show that deep learning algorithms have significant advantages in processing of large-scale images. This paper then examines these deep learning algorithms and reviews previous research that apply deep convolutional neural network methods
which have been widely used in computer vision to extract information on urban built-up areas from high-resolution satellite images. This article also improves the application of computer image processing technology in the field of remote sensing.
Method
2
Semantic image segmentation is crucial in image processing and computer vision. This process recognizes an image at the pixel level and then labels the object category to which each pixel in the image belongs. Based on the deep convolutional neural network oriented to semantic image segmentation
this paper uses the refinement module for the feature map and the attention module of the channel domain to improve the original DeepLab v3 network. The feature refinement module accurately obtains relevant information between pixels and reduces the grid effect. Afterward
the network model processes the feature map through atrous spatial pyramid pooling. The decoding part of the network extracts the attention information of the channel domain and then weighs the low-level features to achieve a better representation and to restore the detailed information. Afterward
the urban built-up area is extracted via the sliding window prediction and full connection conditional random fields methods
both of which can be applied to extract urban built-up areas with better accuracy. However
the use of deep learning algorithms is prone to overfitting and poor robustness. Accordingly
data augmentation and extension are used to enhance the capabilities of the model. Specifically
we use rotation and filter operations while cutting the original training and verification data into 256×256 samples.
Result
2
Extracting information from remote sensing images involves an effective mining and category judgment of such information. The experimental data are taken from Gaofen-2 remote sensing images of Sanya and Haikou cities in Hainan Province
China. These images are specifically taken at the Qiongshan District of Haikou City and at the Tianya District
Jiyang District
and the sea surrounding the Jiaotouding Island of Sanya City. Given their weak sample processing ability
traditional classification algorithms have achieved an accuracy rate of no higher than 85% in the experiments. Meanwhile
deep learning methods
such as SegNet and DeepLab v3
have relatively high accuracy and better performance in extracting urban built-up area information from remote sensing satellite images. By using the refinement module for the feature map and the attention module of the channel domain
this paper improves the accuracy rate of the original DeepLab v3 network by 1.95%. Meanwhile
the proposed method has an accuracy rate of above 93%
a Kappa coefficient of greater than 0.837
a missed detection rate of less than 4.9%
and a false alarm rate of below 2.1%. This method can effectively extract urban built-up areas from large-scale high spatial resolution remote sensing images
and its extraction results are the closest to the actual situation.
Conclusion
2
The comparative experiment shows that the proposed method outperforms others in extracting urban built-up area information from high-resolution remote sensing satellite imagers with diverse spectral information and complex texture structure. Two processing methods are also proposed to significantly improve the accuracy of the model. Both the sliding window method and conditional random fields processing demonstrate an excellent performance in extracting information from high-resolution remote sensing images and show high application value for large-scale remote sensing images.
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