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摘要
Objective: 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: 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 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: 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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