Application of deep learning remote sensing image interpretation technology in cultivated land protection

doi:10.13474/j.cnki.11-2246.2023.0247

Abstract

Abstract: Cultivated land protection is related to national food security, ecological security and social stability. The wide application of deep learning technology in the field of massive data analysis provides a technical foundation for efficient and accurate remote sensing image interpretation. In this paper, the remote sensing image interpretation technology based on deep learning is studied, and a sample library of interpretation samples for training is constructed by using remote sensing image data and corresponding vector data, and an interpretation model based on deep residual network structure is proposed. The practicality of the model has been proved by experiments. In this paper, the change of the main land area in the experimental area is monitored, and the implementation of cultivated land reclamation is verified by comparing the multi-phase image interpretation results and the business data such as the increase and decrease linkage. The results show that deep learning remote sensing image interpretation technology has a wide application prospect in the field of cultivated land protection.

Key words: cultivated land protection, deep learning, high-resolution remote sensing image

CLC Number:

P237

ZHANG Jian, GAO Ya. Application of deep learning remote sensing image interpretation technology in cultivated land protection[J]. Bulletin of Surveying and Mapping, 2023, 0(8): 142-145.

References

[1] 黄瑾,陈红霞. 城乡建设用地增减挂钩政策评价——基于文献综述[J]. 中国国土资源经济,2021,34(9):82-89.
[2] 张良培,武辰. 多时相遥感影像变化检测的现状与展望[J]. 测绘学报,2017,46(10):1447-1459.
[3] 高峰,楚博策,帅通,等. 基于深度学习的耕地变化检测技术[J]. 无线电工程,2019,49(7):571-574.
[4] 吕盛坪,李灯辉,冼荣亨. 深度学习在我国农业中的应用研究现状[J]. 计算机工程与应用,2019,55(20):24-33.
[5] LECUN Y,BENGIO Y,HINTON G. Deep learning[J]. Nature,2015,521(7553):436-444.
[6] LECUN Y,BOTTOU L,ORR G B,et al. Efficient BackProp[M]//ORR G B,MÜLLER K R,Eds. Lecture Notes in Computer Science. Berlin,Heidelberg:Springer Berlin Heidelberg,1998:9-50.
[7] RUDER S. An overview of gradient descent optimization algorithms[EB/OL]. 2016-09-15[2022-09-22].https://arxiv.org/abs/1609.04747.
[8] SHELHAMER E,LONG J,DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4):640-651.
[9] KRIZHEVSKY A,SUTSKEVER I,HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM,2017,60(6):84-90.
[10] SZEGEDY C,LIU Wei,JIA Yangqing,et al. Going deeper with convolutions[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:1-9.
[11] HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas:IEEE,2016:770-778.
[12] TONG Xinyi,XIA Guisong,LU Qikai,et al. Land-cover classification with high-resolution remote sensing images using transferable deep models[EB/OL]. 2018-07-16[2022-09-22]. https://arxiv.org/abs/1807.05713.
[13] WANG Guoli,FAN Bin,XIANG Shiming,et al. Aggregating rich hierarchical features for scene classification in remote sensing imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,10(9):4104-4115.
[14] HUANG Bo,ZHAO Bei,SONG Yimeng. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery[J]. Remote Sensing of Environment,2018,214:73-86.
[15] CHEN L C,PAPANDREOU G,SCHROFF F,et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. 2017-06-17[2022-09-21]. https://arxiv.org/abs/1706.05587.