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    Journals >Laser & Optoelectronics Progress >Volume 56 >Issue 11 >Page 112801 > Article
    • Laser & Optoelectronics Progress
    • Vol. 56, Issue 11, 112801 (2019)
    Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images
    Wenxiu Teng1,**, Ni Wang2,3,*, Taisheng Chen2,3, Benlin Wang2,3,4..., Menglin Chen2,3 and Huihui Shi3|Show fewer author(s)
    Author Affiliations
  • 1 College of Forest, Nanjing Forestry University, Nanjing, Jiangsu 210037, China
  • 2 School of Geographic Information and Tourism, Chuzhou University, Chuzhou, Anhui 239000, China
  • 3 Anhui Engineering Laboratory of Geographical Information Intelligent Sensor and Service, Chuzhou, Anhui 239000, China
  • 4 School of Earth Sciences and Engineering, Hohai University, Nanjing, Jiangsu 210098, China
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    DOI: 10.3788/LOP56.112801 Cite this Article Set citation alerts
    Wenxiu Teng, Ni Wang, Taisheng Chen, Benlin Wang, Menglin Chen, Huihui Shi. Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images[J]. Laser & Optoelectronics Progress, 2019, 56(11): 112801 Copy Citation Text show less
    Framework of proposed method. (a) High spatial resolution remote sensing image dataset; (b) source deep convolutional neural network; (c) unsupervised adversarial domain adaptation; (d) remote sensing image scene classification
    Fig. 1. Framework of proposed method. (a) High spatial resolution remote sensing image dataset; (b) source deep convolutional neural network; (c) unsupervised adversarial domain adaptation; (d) remote sensing image scene classification
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    Sample images of UC-Merced dataset
    Fig. 2. Sample images of UC-Merced dataset
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    Sample images of WHU-RS dataset
    Fig. 3. Sample images of WHU-RS dataset
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    Classification confusion matrix of UC-Merced dataset. (a) Classification accuracy of source domain; (b) classification accuracy of domain adaptation
    Fig. 4. Classification confusion matrix of UC-Merced dataset. (a) Classification accuracy of source domain; (b) classification accuracy of domain adaptation
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    Classification confusion matrix of WHU-RS dataset. (a) Classification accuracy of source only; (b) classification accuracy of domain adaptation
    Fig. 5. Classification confusion matrix of WHU-RS dataset. (a) Classification accuracy of source only; (b) classification accuracy of domain adaptation
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    Large class(six)Subclass(seventy-one)
    Construction landcity_building, container, storage_room, pipeline, town, baseball_diamond, basketball_court, golf_course, tennis_court, ground_track_field, church, commercial_area, industrial_area, mobile_home_park, palace, stadium, thermal_power_station, dense_ residential, medium_residential, sparse_residential, Square, Center, Park, Resort, School, Playground
    Ultivated landgreen_farmland, dry_farm, bare_land, circular_farmland, rectangular_ farmland, terrace
    Transportationairplane, airport_runway, avenue, highway, harbor, parkinglot, crossroads, bridge, airport, overpass, railway, railway_station, ship, roundabout
    Water areabeach, dam, hirst, lakeshore, river, sea, stream, island, lake, sea_ice, wetland
    Woodlandartificial_grassland, sparse_forest, forest, mangrove, sapling, river_protection_forest, shrubwood, chaparral, meadow
    Otherdesert, snow_mountain, mountain, sandbeach, cloud
    Table 1. Dataset category for remote sensing image scene classification of source domain
    Layer nameLayer typeOutput size /(pixel×pixel×pixel)
    Source /targetDCNNInput224×224×3
    Convolution×2224×224×64
    Map pooling112×112×64
    Convolution×2112×112×128
    Map pooling56×56×128
    Convolution×356×56×256
    Map pooling28×28×256
    Convolution×328×28×512
    Map pooling14×14×512
    Convolution×314×14×512
    Map pooling7×7×512
    Fully connected1×1×1024
    Softmax1×1×18/1×1×21
    Table 2. Deep convolution neural network structure of source domain and target domain
    Layer nameLayer typeOutput size /(pixel×pixel×pixel)
    DiscriminatorFully connected1×1×1024
    Fully connected1×1×512
    Fully connected1×1×2
    Softmax1×1×2
    Table 3. Structure of discriminator
    AlgorithmSource onlyMMDDANNProposed
    UC-Merced70.4382.1981.6286.71
    WHU-RS87.0694.8393.8397.41
    Table 4. Classification accuracy of each algorithm%
    Abstract
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    References (62)
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    Wenxiu Teng, Ni Wang, Taisheng Chen, Benlin Wang, Menglin Chen, Huihui Shi. Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images[J]. Laser & Optoelectronics Progress, 2019, 56(11): 112801
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    Paper Information
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