Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (6): 2131-2137.doi: 10.13229/j.cnki.jdxbgxb.20240503

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Semantic segmentation algorithm for multi temporal high⁃resolution satellite remote sensing images

Ying YU1,2(),Chun-ping WANG1,2,Ren-ke KOU1,Bo-xiong YANG2,Lei WANG3,4,Fu-jun ZHAO2,Qiang FU1()   

  1. 1.Army Engineering University of PLA，Shijiazhuang 050003，China
    2.Academician Workstation of Chunming Rong，University of Sanya，Sanya 572022，China
    3.Key Laboratory of Earth Observation of Hainan Province，Hainan Aerospace Information Research Institute，Sanya 572029，China
    4.Key Laboratory of Earth Observation of Hainan Province，Aerospace Information Research Institute，Chinese Academy of Sciences，Beijing 100094，China
  • Received:2024-05-10 Online:2025-06-01 Published:2025-07-23
  • Contact: Qiang FU E-mail:yingyu@sanyau.edu.cn;Fu_Qiang@aeu.edu.cn

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Abstract:

In order to solve the problem of inaccurate recognition of various semantic objects in remote sensing images using a single temporal low resolution image， a multi temporal high-resolution satellite remote sensing image semantic segmentation algorithm was proposed. By solving the multi temporal resolution of image information， remote sensing targets are partitioned， and various semantic objects and features of remote sensing images are accurately identified and extracted. Based on the definition of scale function， the segmentation weight is calculated to realize accurate recognition and segmentation of semantic objects in remote sensing images. The experimental results show that the recognition accuracy of semantic objects is significantly improved by the proposed method， and the maximum numerical difference between the experimental value and the real value of each semantic object content in the segmented ground object information does not exceed 0.2%， which provides strong support for the application of remote sensing images.

Key words: multi temporal high-resolution, satellite remote sensing images, semantic segmentation, significant remote sensing areas, scale function, segmentation weight

CLC Number: 

  • TN929

Fig.1

Schematic diagram of the image remote sensing target partition"

Fig.2

Image of satellite remote sensing image data set"

Table 1

Annotation parameters of the semantic information of the satellite remote sensing data set"

标记地物信息名称标记颜色BRG颜色值
1背景物（0，0，0）
2植物（127，0，255）
3建筑结构（0，255，255）
4水系结构（255， 0，191）
5连通道路（60，220，20）

Fig.3

Satellite remote sensing image data set after color annotation"

Fig.4

Portion of semantic objects in the satellite remote sensing image data set"

Fig.5

Comparison of the semantic segmentation results of satellite remote sensing images"

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