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Remote Sensing Technology and Application  2022, Vol. 37 Issue (4): 789-799    DOI: 10.11873/j.issn.1004-0323.2022.4.0789
Reclamation Detection and Recognition of High Resolution Satellite Remote Sensing Image based on Deep Learning
Fengshi Yu1(),Yi Sui1(),Changying Wang1,Jialan Chu2
1.School of Computer Science and Technology,Qingdao University,Qingdao 266071,China
2.School of Data Science and Software Engineering,Qingdao University,Qingdao 266071,China
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Based on high-resolution satellite remote sensing images, automatic and accurate extraction of land use status of reclamation is an important technical means to realize the intensive use of reclamation. In view of the complex features of high-resolution satellite remote sensing images, the traditional method of manually extracting features is difficult to meet the actual needs of business departments. A framework of reclamation detection and recognition based on deep learning is proposed. The framework uses the multi constrained variant structure of U-Net network, and to solve the problem of inconsistent classification caused by complex features of high-resolution remote sensing images, full connection conditional random field and image corrosion operation are introduced to post-processing the segmentation results. The high-resolution satellite remote sensing images of Tianjin Binhai New Area in 2016 and 2020 were used as data sources to verify. The experimental results show that the overall accuracy rate, F1 score, kappa coefficient and mIoU of reclamation are 96.73%, 92.87%, 90.28% and 86.82% respectively. On this basis, the dynamic change characteristics of land use in the reclamation area are analyzed and extracted, which provides effective technical support for the intensive use and management of reclamation.

Key words:  Reclamation      Deep learning      Detection and identification      U-Net     
Received:  31 August 2020      Published:  28 September 2022
Corresponding Authors:  Yi Sui     E-mail:;
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Fengshi Yu
Yi Sui
Changying Wang
Jialan Chu

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Fengshi Yu,Yi Sui,Changying Wang,Jialan Chu. Reclamation Detection and Recognition of High Resolution Satellite Remote Sensing Image based on Deep Learning. Remote Sensing Technology and Application, 2022, 37(4): 789-799.

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Fig.1  Classic U-Net network structure16
Fig.2  Multi constrained variant U-Net (MCFCN) network architecture
Fig.3  Fully connected condition random field processing flow
Fig.4  Comparison of post-processing results
Fig.5  Flow chart of reclamation land detection
Fig.6  The division of research area, test set and training set area
Table 1  Selection of constraint parameters in loss function
Table 2  Performance comparison of models on validation set under different parameters
2016年SVM0.913 60.723 60.657 90.605 0
FCN8s0.928 40.839 70.779 80.729 2
U-Net0.957 40.908 60.872 80.834 4
Linknet0.957 70.910 00.874 30.836 7
MCFCN20.958 10.915 30.879 70.844 8
MCFCN2+后处理0.962 10.924 30.892 00.860 1


SVM0.876 00.684 90.589 00.547 5
FCN8s0.931 70.844 00.789 90.735 8
U-Net0.955 50.905 50.870 40.829 1
Linknet0.955 70.904 00.869 40.826 9
MCFCN20.961 20.914 10.883 50.843 7
MCFCN2+后处理0.967 30.928 70.902 80.868 2
Table 3  Comparison of test area results
UNet3.408 994.77
Linknet8.328 675.32
MCFCN23.414 888.31
Table 4  Model parameter scale
Fig.7  Comparison of test area results between 2016 and 2020
Fig.8  Comparison results after post-processing
Fig.9  Study area division and results in 2016 and 2020
Table 5  Land use change between 2016 and 2020
Fig.10  Land area and land use area in 2016 and 2020
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