Reclamation Detection and Recognition of High Resolution Satellite Remote Sensing Image based on Deep Learning

doi:10.11873/j.issn.1004-0323.2022.4.0789

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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Abstract

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

P756.8

Corresponding Authors: Yi Sui E-mail: 2019020589@qdu.edu.cn;suiyi@qdu.edu.cn

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Cite this article:

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.

URL:

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.4.0789 OR http://www.rsta.ac.cn/EN/Y2022/V37/I4/789

Fig.1 Classic U-Net network structure^［16］

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

Table 3 Comparison of test area results

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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