基于语义分割网络的高分遥感影像城市建成区提取方法研究与对比分析

Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network

城市建成区的提取对城市发展规划有着重要的作用。为了找出能兼顾效率和识别准确率的基于卷积神经网络的遥感影像城市建成区提取方法,从神经网络结构的原理出发,对多种语义分割网络的内部结构进行对比分析,并针对语义分割网络分别进行训练及结果比较。实验结果表明,ShelfNet-50网络能够在训练速度最快的同时保证很高的识别准确率,在训练时长仅需14 h的同时达到了77%的前景分割精度,且ShelfNet-50网络预测的结果也与相应的遥感影像数据高度吻合。实验说明ShelfNet-50网络可应用于高分遥感影像的城市建成区提取研究。

The extraction of urban built-up areas plays an important role in urban development planning.To find out the method of extracting remote sensing image urban built-up area based on convolutional neural network which can balance efficiency and recognition accuracy,the authors started with the principle of neural network structure and compared as well as analyzed the internal structure of multiple semantic segmentation networks.The semantic segmentation network was trained separately and the results were comparatively studied.The experimental result shows that the ShelfNet-50 network could ensure high recognition accuracy while training speed,achieved 77%foreground segmentation accuracy while training time was only 14 hours,and the result of ShelfNet-50 network prediction was also highly consistent with the corresponding remote sensing image data.The experiment confirms that ShelfNet-50 network can be applied to high-resolution remote sensing image urban built-up area extraction problems.