Automatic monitoring of farmland occupation by farm house based on deep learning network

doi:10.13474/j.cnki.11-2246.2022.0076

Abstract

Abstract: In recent years,illegal occupation of arable land has been repeatedly prohibited.How to use artificial intelligence and other new-generation information technology to quickly figure out the number of illegal occupation of arable land and build houses in rural areas,and achieve"early detection,early stop,strict investigation and punishment ",is one of the current research difficulties in the work of rectifying the illegal occupation of farmland in rural areas.This paper preprocesses high-resolution natural resource image data,and then builds an automated monitoring model based on a deep learning network.Thirdly,it applies model to predict and GIS optimization and spatial overlay of output results.Experimental results show that this method can quickly detect illegal houses that are suspected of occupying cultivated land,and provides intelligent technology options for sticking to the bottom line of" not breaking through the red line of cultivated land",and can serve the work of rectifying building houses on the cultivated land in rural areas.

Key words: natural resource monitoring;basic farmland;deep learning;U-Net network;high-resolution remote sensing image

CLC Number:

P237

GAO Ming, ZHOU Xinxin, LIU Qi, YANG Guangdi, WU Changbin. Automatic monitoring of farmland occupation by farm house based on deep learning network[J]. Bulletin of Surveying and Mapping, 2022, 0(3): 47-53.

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URL: http://tb.chinasmp.com/EN/10.13474/j.cnki.11-2246.2022.0076

http://tb.chinasmp.com/EN/Y2022/V0/I3/47

References

[1] 钱凤魁,王秋兵,边振兴,等.永久基本农田划定和保护理论探讨[J].中国农业资源与区划, 2013, 34(3):22-27.
[2] 王海波,马明国.基于遥感的湖泊水域动态变化监测研究进展[J].遥感技术与应用, 2009, 24(5):674-684.
[3] KARPATNE A, JIANG Z, VATSAVAI R R, et al. Monitoring land-cover changes:a machine-learning perspective[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2):8-21.
[4] 王立娟,靳晓,贾虎军,等.基于国产高分卫星数据的矿山环境变化检测[J].国土资源遥感, 2018, 30(3):151-158.
[5] 李亮,舒宁,王凯,等.融合多特征的遥感影像变化检测方法[J].测绘学报, 2014, 43(9):945-953, 959.
[6] HE K, ZHANG X, REN S, et al. Deep Residual Learning for Image Recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Seattle,WA:IEEE, 2016:770-778.
[7] LONG J, SHELHAMER E, DARRELL T, et al. Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA:IEEE, 2015:3431-3440.
[8] CHEN G Z, ZHANG X D, WANG Q, et al. Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-highresolution remote sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(5):1633-1644.
[9] FAUVEL M, BENEDIKTSSON J A, CHANUSSOT J, et al. Spectral and spatial classification of hyperspectral data using svms and morphological profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11):3804-3814.
[10] MALISIEWICZ T, GUPTA A, EFROS A A, et al. Ensemble of exemplar-SVMs for object detection and beyond[C]//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona, Spain:IEEE, 2011:89-96.
[11] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab:semantic image segmentation with deep convolutional nets, Atrous Convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4):834-848.
[12] ZHOU L, ZHANG C, WU M, et al. D-LinkNet:LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT:IEEE, 2018:192-196.
[13] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Proceeding of 2015 International Conference on Medical Image Computing and Computer-assisted Intervention.[S. I.]:Springer,2015:234-241.
[14] KAMIYA R, HOTTA K, ODA K, et al. Road detection from satellite images by improving U-Net with difference of features[C]//Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods. Funchal, PortugaL:SCITEPRESS,2018:603-607.
[15] ZHANG Z, LIU Q, WANG Y. Road extraction by deep residual U-Net[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5):749-753.
[16] 赵俊娟,尹京苑,单新建.基于形状特征的高分辨率遥感影像目标分割[J].测绘通报, 2005(1):10-13.
[17] 崔建军,隋立春,徐花芝,等.基于边缘检测算法的LiDAR数据建筑物提取[J].测绘科学技术学报, 2008, 25(2):98-100.
[18] 曲延云,郑南宁,李翠华,等.基于支持向量机的显著性建筑物检测[J].计算机研究与发展, 2007, 44(1):141-147.
[19] LEI H O U, YIN D, XIAOJIAN Y O U. An automatic building detection method from remote sensing images[J]. Computer Simulation, 2006, 23(4):184-188.
[20] MATIKAINEN L, HYYPPA J, AHOKAS E, et al. Automatic detection of buildings and changes in buildings for updating of maps[J]. Remote Sensing, 2010, 2(5):1217-1248.
[21] 李东子,范大昭,苏亚龙.结合Faster R-CNN模型的遥感影像建筑物检测[J].测绘科学技术学报, 2018, 35(4):389-394.
[22] 伍广明,陈奇, SHIBASAKI R,等.基于U型卷积神经网络的航空影像建筑物检测[J].测绘学报, 2018, 47(6):864-872.
[23] JI S, SHEN Y, LU M, et al. Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples[J]. Remote Sensing, 2019, 11(11):1343-1362.