• Acta Optica Sinica
  • Vol. 39, Issue 12, 1210001 (2019)
Ende Wang1、2、3, Kai Qi1、2、3、4、*, Xuepeng Li1、2、3, and Liangyu Peng1、2、3
Author Affiliations
  • 1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 2Institute for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110169, China
  • 3Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 4College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
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    DOI: 10.3788/AOS201939.1210001 Cite this Article Set citation alerts
    Ende Wang, Kai Qi, Xuepeng Li, Liangyu Peng. Semantic Segmentation of Remote Sensing Image Based on Neural Network[J]. Acta Optica Sinica, 2019, 39(12): 1210001 Copy Citation Text show less
    FCN structural diagram
    Fig. 1. FCN structural diagram
    ResNet basic structural unit
    Fig. 2. ResNet basic structural unit
    Diagram of ResNet18 structure
    Fig. 3.

    Diagram of ResNet18 structure

    Main structure of network
    Fig. 4. Main structure of network
    Diagram of N×N channel structure
    Fig. 5. Diagram of N×N channel structure
    Two-channel feature fusion with different scales
    Fig. 6. Two-channel feature fusion with different scales
    Downsampling and upsampling of location index max pool
    Fig. 7. Downsampling and upsampling of location index max pool
    Partial images of data sets and corresponding visual labels
    Fig. 8. Partial images of data sets and corresponding visual labels
    Partial data set images and their visual mark display after cutting
    Fig. 9. Partial data set images and their visual mark display after cutting
    Partial original images and display of flipped and rotated images
    Fig. 10. Partial original images and display of flipped and rotated images
    Visual classification results of remote sensing images by different classification algorithms
    Fig. 11. Visual classification results of remote sensing images by different classification algorithms
    Visual classification results of channel 1, channel 1+2, and overall structure
    Fig. 12. Visual classification results of channel 1, channel 1+2, and overall structure
    CategoryVegetationBuildingWaterRoadOthers
    Vegetation9225390393999718781875047281427
    Building7837261250580050563126407290737
    Water18293690152069612676129298
    Road201965049050493256576176714
    Others834751446908348417382672437
    Table 1. Obfuscation matrix of classification results of proposed algorithmpixel
    AlgorithmClassification accuracy /%RKappa
    VegetationBuildingWaterRoadOthersOA
    FCN-8s71.8871.4370.0969.9771.2371.300.6603
    Unet80.9280.2780.1079.8980.5280.440.7522
    SegNet83.6683.1882.6482.5283.0783.210.7810
    Channel 181.1681.2381.4780.9781.3281.200.7623
    Channel 1+289.5689.7889.5889.1689.7689.630.8406
    Ours90.7790.8290.1890.3090.5790.680.8595
    Table 2. Classification accuracy and RKappa of different algorithms
    AlgorithmTotal number of parameters /106Forward time /msTrain time /h
    FCN-8s134.32215.56
    Unet23.6564.72
    SegNet29.4784.88
    Channel 115.3504.55
    Channel 1+230.51085.06
    Ours30.51162.90
    Table 3. Convolution kernel parameters, single forward propagation time, and training time of different algorithms
    Ende Wang, Kai Qi, Xuepeng Li, Liangyu Peng. Semantic Segmentation of Remote Sensing Image Based on Neural Network[J]. Acta Optica Sinica, 2019, 39(12): 1210001
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