模态框(Modal)标题

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模态框(Modal)标题

ISSN 1004-0323
CN 62-1099/TP
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Figure/Table detail

Different Spatial Resolutions based on Object-oriented CNN and RF Research on Agricultural Greenhouse Extraction from Remote Sensing Images
Xinyi LIN, Xiaoqin WANG, Zixia TANG, Mengmeng LI, Ruijiao WU, Dehua HUANG
Remote Sensing Technology and Application, 2024, 39(2): 315-327.   DOI: 10.11873/j.issn.1004-0323.2024.2.0315

RFOCNNFT
1 m2 m4 m8 m10 m16 m1 m2 m4 m8 m10 m16 m
1 m1 m
2 m0.872 m1.36
4 m0.560.694 m5.622.09
8 m1.630.741.088 m6.047.670.96
10 m3.102.551.672.8010 m3.285.522.613.58
16 m5.354.533.745.002.4116 m9.619.395.725.317.83
Table 3 McNemar test results based on RF method and OCNNFT method(significance α=0.05)
Other figure/table from this article
  • Fig.1 Distribution of key research areas and sample points in Fu'an City
  • Table 1 Remote sensing image acquisition in the study area
  • Fig.2 Example of local true color band combinations of canopies with different spatial resolution images
  • Fig.3 Basic structure of the residual unit
  • Fig.4 Object-oriented CNN method(OCNNFT) flowchart
  • Table 2 Feature variables for agricultural greenhouse extraction
  • Fig.5 Fine-tuning results of CNN models on different resolution images
  • Fig.6 Greenhouse distribution based on RF method and OCNNFT method
  • Fig. 7 Detailed diagram of classification effect of different methods
  • Fig.8 PA, UA, OA and F-score of agricultural greenhouses under different resolution images
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