收稿：2021-02-20，
纸质出版：2023-04-07
移动端阅览
城市建筑物自动提取是高分辨率遥感影像理解的重要研究方向，其对于城市基础地理信息更新和城市生态保护均具有重要的应用价值和实际意义。然而由于城市场景的复杂性和建筑物形态的多样性降低了空间特征的综合表达能力，成为了制约城市建筑物自动提取的瓶颈问题。为此，本研究在综合分析城市建筑物不同模式空间特征的基础上，提出了一种多模式形态学序列特征和多源先验信息协同的城市建筑物高分遥感自动提取方法。该方法在提取高分遥感多模式形态学序列特征的基础上，引入多源先验信息构建自适应分割模型对其进行自适应分割与信息融合，从而实现城市建筑物信息的自动提取。实验结果表明，本文方法能够准确且自动的提取城市建筑物信息，结果的准确性均优于DMPs和DAPs算法。
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.
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