摘要
目的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 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
作者
侯博文
闫冬梅
郝伟
黄青青
苏秀琴
李青雯
Hou Bowen;Yan Dongmei;Hao Wei;Huang Qingqing;Su Xiuqin;Li Qingwen(Xi'an Institute of Optics and Precision Mechanics,Chinese Academy of Sciences,Xi'an 710119,China;Institute of Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;Key Laboratory of Earth Observation,Institute of Remote Sensing of Sanya,Sanya 572029,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《中国图象图形学报》
CSCD
北大核心
2020年第12期2677-2689,共13页
Journal of Image and Graphics
基金
海南省重点研发计划项目(ZDYF2018001)。
关键词
卷积神经网络
注意力机制
遥感图像
城镇建成区
信息提取
convolutional neural network(CNN)
attention mechanism
remote sensing image
urban built-up area
information extraction
