سابِق|تال

High-resolution remote sensing extraction of urban buildings based on morphological sequences and multi-source a priori information

  • role： First author第一作者
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

  • Email：lizhi@lreis.ac.cn
  • Introduction：李治，研究方向为多源遥感信息协同提取与城市生态应用。E-mail：lizhi@lreis.ac.cn
LI Zhi,  
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

SUI Zhengwei,  
  • role： Corresponding author通信作者
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

  • Email：fuqiaoyan1970@163.com
  • Introduction：傅俏燕，研究方向为资源环境遥感技术与应用。E-mail： fuqiaoyan1970@163.com
FU Qiaoyan*,  
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

ZHENG Jinjin,  
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

BU Tong

ملخص

Urban building extraction is an important research direction for the understanding and target recognition of high-resolution optical remote sensing images. Realizing accurate automatic building extraction has important application value and practical significance for the acquisition and update of basic urban geographic information. Given the complexity of urban scenes and the diversity of building forms, the characteristics of urban buildings are difficult to express fully, and the generalization ability of samples is insufficient, thus becoming a bottleneck problem for the automatic extraction of urban buildings. In this study, a multi-modal morphological-sequence-feature synergy method is proposed to utilize fully the advantages of each morphological sequence feature from different modes and mine the high-dimensional spatial information of urban buildings jointly. On this basis, we introduce multi-source a priori information and develop an adaptive segmentation model method based on multi-source a priori information to achieve the automatic recognition of urban buildings. This method can help avoid the limitations, such as errors and low efficiency, brought by manual threshold selection.The process of the method for urban building extraction proposed in this study is mainly divided into four steps. First, the differential morphological structure sequence features and differential morphological attribute sequence features of remote sensing images are calculated on the basis of high-resolution remote sensing images. Second, the feature selection model is constructed to optimize the differential morphological structure sequence features and differential morphological attribute sequence features. Then, the adaptive segmentation model is constructed on the basis of the multi-source a priori information products. The adaptive segmentation of the preferred features is performed to obtain the initial information of urban buildings. Finally, the voting method is used to fuse the initial information of urban buildings at the decision level to obtain the final urban building extraction results.The performance of the proposed method shows that the average extraction accuracy and kappa coefficient of the research method in this study are 91.3% and 0.87, which are 7.8% and 5.5% and 0.1 and 0.07 higher than the 85.7% and 83.5% and 0.81 and 0.78 of DMPs and DAPs extraction methods, respectively. Thus, the results demonstrate the effectiveness of the method in the automatic extraction of urban buildings in this study.The method in this research achieves rapid, automated, and high-precision urban building information acquisition and update. Furthermore, it provides a method reference template for rapid building detection and update in more cities. In the subsequent research, further quantitative evaluation of each type of a priori information product is needed to clarify the role of different information products in automatic building extraction, as it can improve the accuracy and automation of building extraction further.

مفهوم

Morphological structural sequence features;morphological attribute sequence features;feature significance level model;adaptive segmentation model;decision-level information fusion;a priori information

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