• Laser & Optoelectronics Progress
  • Vol. 57, Issue 10, 101017 (2020)
Yanfei Peng**, Jinye Mei*, Kaixin Wang, Lingling Zi, and Yu Sang
Author Affiliations
  • School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
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    DOI: 10.3788/LOP57.101017 Cite this Article Set citation alerts
    Yanfei Peng, Jinye Mei, Kaixin Wang, Lingling Zi, Yu Sang. Remote Sensing Image Retrieval Based on Regional Attention Mechanism[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101017 Copy Citation Text show less
    Framework of the retrieval system
    Fig. 1. Framework of the retrieval system
    Regional scale of R-MAC
    Fig. 2. Regional scale of R-MAC
    Framework of ResNet
    Fig. 3. Framework of ResNet
    Framework diagram of our network
    Fig. 4. Framework diagram of our network
    UCM dataset
    Fig. 5. UCM dataset
    SIRI dataset
    Fig. 6. SIRI dataset
    Retrieval effect when return 20 images
    Fig. 7. Retrieval effect when return 20 images
    Retrieval effect when return 40 images
    Fig. 8. Retrieval effect when return 40 images
    Comparison of average recalls. (a) UCM dataset; (b) SIRI dataset
    Fig. 9. Comparison of average recalls. (a) UCM dataset; (b) SIRI dataset
    Average precision at different scales
    Fig. 10. Average precision at different scales
    MethodVGG16ResNet101DenseNet121Ourmethod
    UCM71.275.978.996.8
    SIRI70.075.774.488.6
    Average70.675.876.792.7
    Table 1. Comparison of mAP of different methodsunit: %
    Image size1.00.90.80.70.60.5
    mAP /%88.685.384.782.177.370.9
    Table 2. Comparison of mAP of different image sizes
    MethodVGG16DenseNet121ResNet101Our method
    RANRAN+MDRAN+MD+QE
    Agricultural928681979797
    Airplane698384959695
    Baseball diamond433846100100100
    Beach8185909899100
    Building234446697365
    Chaparral929995100100100
    Dense residential294627778582
    Forest809284100100100
    Freeway434430888992
    Golf course334151949597
    Harbor4375759898100
    Intersection264540969598
    Medium residential377855808280
    Mobile home park698039949595
    Overpass486443949592
    Parking lot627760999395
    River254250888593
    Runway576549878396
    Sparse residential494541798585
    Storage tanks253125928695
    Tennis court3836339899100
    Average50.761.754.591.691.993.2
    Table 3. Average accuracy of different methods on the UCM datasetunit: %
    MethodVGG16DenseNet121ResNet101Our method
    RANRAN+MDRAN+MD+QE
    Agriculture597329979599
    Commercial515967878994
    Harbor505465606267
    Idle land274339858588
    Industrial424966787375
    Meadow293227646969
    Overpass718189979798
    Park354344777983
    Pond394946666276
    Residential516048878790
    River343035899095
    Water939596979898
    Average48.455.754.382.082.286.0
    Table 4. Average accuracy of different methods on the SIRI datasetunit: %
    Yanfei Peng, Jinye Mei, Kaixin Wang, Lingling Zi, Yu Sang. Remote Sensing Image Retrieval Based on Regional Attention Mechanism[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101017
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