• Laser & Optoelectronics Progress
  • Vol. 59, Issue 12, 1210015 (2022)
Daming Zhang1、2、*, Xueyong Zhang1、2, Huayong Liu1, and Lu Li1
Author Affiliations
  • 1School of Mathematics & Physics, Anhui Jianzhu University, Hefei 230022, Anhui , China
  • 2Key Laboratory of Architectural Acoustic Environment of Anhui Higher Education Institutes, Hefei 230601, Anhui , China
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    DOI: 10.3788/LOP202259.1210015 Cite this Article Set citation alerts
    Daming Zhang, Xueyong Zhang, Huayong Liu, Lu Li. Remote Sensing Image Segmentation Using Super-Pixel and Dot Product Representation of Graphs[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1210015 Copy Citation Text show less

    Abstract

    The image segmentation method using division-combination mitigates the limitations of the traditional pixel-based remote sensing image segmentation algorithm, such as noise interference, low segmentation efficiency, and poor segmentation effect. Thus, this paper proposes a new split-merge-based remote sensing image segmentation method using the super-pixel and dot product representation of graphs. First, the image is divided into super-pixels using the simple linear iterative clustering (SLIC) algorithm. Second, the texture feature of each super-pixel area is measured and distance between any two areas is calculated with respect to spatial proximity. Third, each super-pixel area is mapped as a vertex of the graph. Therefore, the dot product representation of graphs is modified and used to construct a similarity matrix; thereafter, all vertices (i.e., super-pixel areas) are mapped as new vectors clustered by angular-based k-means algorithm to get the final segmentation results. The experimental results show that the proposed method has stable segmentation results, improves the accuracy of the segmentation, and achieves a better visual segmentation effect.
    Daming Zhang, Xueyong Zhang, Huayong Liu, Lu Li. Remote Sensing Image Segmentation Using Super-Pixel and Dot Product Representation of Graphs[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1210015
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