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SHAO Zhenfeng, SUN Yueming, XI Jiangbo, LI Yan. Intelligent Optimization Learning for Semantic Segmentation of High Spatial Resolution Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(2): 234-241. DOI: 10.13203/j.whugis20200640
Citation: SHAO Zhenfeng, SUN Yueming, XI Jiangbo, LI Yan. Intelligent Optimization Learning for Semantic Segmentation of High Spatial Resolution Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(2): 234-241. DOI: 10.13203/j.whugis20200640
shu

Intelligent Optimization Learning for Semantic Segmentation of High Spatial Resolution Remote Sensing Images

  • 1.

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • 2.

    College of Geology Engineering and Surveying, Chang'an University, Xi'an 710054, China

  • 3.

    Inner Mongolia Electronic Information Vocational Technical College, Huhhot 010070, China

Funds: 

The National Natural Science Foundation of China 41771454

The National Natural Science Foundation of China 61806022

the Jiangxi Province 03 Special Project and 5G Project 20212ABC03A09

the Natural Science Foundation of Inner Mongolia Autonomous Region 2019MS04017

the Scientific Research Project of Colleges and Universities in Inner Mongolia Autonomous Region NJZY20277

the State Key Laboratory of Geo-Information Engineering SKLGIE2018-M-3-4

the Fundamental Research Funds for the Central Universities, Chang'an University 300102269103

the Fundamental Research Funds for the Central Universities, Chang'an University 300102269304

the Fundamental Research Funds for the Central Universities, Chang'an University 300102269205

More Information
  • Author Bio:

    SHAO Zhenfeng, PhD, professor, specializes in research and education of smart city and urban remote sensing. E-mail: shaozhenfeng@whu.edu.cn

  • Corresponding author:

    SUN Yueming, master. E-mail: sunyueming@whu.edu.cn

    XI Jiangbo, PhD, associate professor. E-mail: xijiangbo@chd.edu.cn

  • Received Date: December 15, 2020
  • Published Date: February 04, 2022
    Graphical Abstract
    • Abstract
  •   Objectives   With the hardware development of remote sensing sensors, the high spatial resolution remote sensing images are widely used. In the actual classification, typical classification algorithms can not achieve high accuracy and efficiency, while deep learning semantic segmentation algorithms can not achieve good generalization.
      Methods   In order to adapt to the large-scale high-resolution images, we design a semantic segmentation model of simulated annealing hyperparameter optimization and depthwise separable convolution based on U-Net. Firstly, the depthwise separable convolutional module is used to extract the features on the baseline. Then, the intelligent optimization learning model based on simulated annealing searches the global optimal solution of hyperparameters. Finally, experiments were carried out in ISPRS2D and Gaofen image dataset (GID).
      Results   Compared to other classification methods, the proposed method achieves the highest classification accuracy in buildings, low vegetation, trees, cars and overall accuracy, and the total accuracy of classification results reaches 86.5% in ISPRS2D. And in the classification results of GID, the accuracies of the proposed method in water, grassland, forest and general classification are all greatly improved. The mean intersection over union of the proposed method is also higher than that of other methods.
      Conclusions   The results demonstrated the higher efficiency, higher accuracy and robustness of the proposed method.
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    • References (21)
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