سابِق|تال

Automatic road extraction from high-resolution remote sensing images assisted by vector data

  • role： First author第一作者
  • Affiliation：

    School of geospatial information, PLA information engineering university, Zhengzhou 450001, China

  • Email：dinglei14@outlook.com
  • Introduction：丁磊(1991—    )，男，博士研究生，研究方向为遥感影像解译。E-mail：dinglei14@outlook.com
DING Lei,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of geospatial information, PLA information engineering university, Zhengzhou 450001, China

  • Email：zbm1960@163.com
  • Introduction：张保明(1961—    )，男，教授，研究方向为数字摄影测量及遥感数据应用。E-mail：zbm1960@163.com
ZHANG Baoming*,  
  • Affiliation：

    School of geospatial information, PLA information engineering university, Zhengzhou 450001, China

GUO Haitao,  
  • Affiliation：

    School of geospatial information, PLA information engineering university, Zhengzhou 450001, China

LU Jun

ملخص

Considering the existence of both detailed information and interfering objects, research on recognition and extraction of roads from high-resolution remote sensing images remains in the exploration phase. The assistance of vector data during road extraction solves the difficulty of obtaining initial information. Thus, reliable training data can be selected. Therefore, a road extraction method that is supported by vector data is proposed, which effectively provides information that is included in the vector data and extract roads automatically from high-resolution remote sensing images. After the use of the mean shift filter to preprocess images, candidate seed points are acquired from vector data, among which false candidate points are later excluded via shape features of homogeneous areas. Then, negative sample points are selected for the conduct of Bayesian classification. After which, Neighborhood Centroid Voting is adopted to extract road centerlines from classified images. Finally, road centerlines are transformed into vector data by using a method that combines pixel tracking and direction estimation, and a segment linking and burr removing method is proposed based on geometric analysis of vectors. Experiments are conducted on a collection of high-resolution images in which roads have diverse types and distribution features. For an ideal case with clear and continuous roads, the extraction quality is up to 97.35%. For images with occlusions and interferences, the extraction quality stays above 80%, thereby indicating the effectiveness and robustness of the proposed method. A road extraction method that is supported by vector data has been proposed in this study. This method uses the information concluded based on vector data to guide the road extraction process, thus free of manual operation. Experimental results suggest that this method can automatically extract roads from high-resolution remote sensing images with accuracy and robustness, thereby being able to adapt to roads with diverse radiation and distribution features.

مفهوم

road extraction;vector data;naïve Bayesian classification;Neighborhood Centroid Voting;vectorization;segment linking

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