Classification method of cultivated land based on UAV visible light remote sensing
Keywords:
UAV, visible band, remote sensing, extraction of cultivated land area, object oriented methodAbstract
The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture. UAV remote sensing has the advantages of low cost use, simple operation, real-time acquisition of remote sensor images and high ground resolution. It is difficult to separate cultivated land from other terrain by using only a single feature, making it necessary to extract cultivated land by combining various features and hierarchical classification. In this study, the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information, shape information and position information of farmland. Based on the vegetation index, texture information and shape information in the visible light band, the object-oriented method was used to study the best scheme for extracting cultivated land area. After repeated experiments, it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters. Uncultivated crops and other features are separated by using the band information and texture information. The overall accuracy of this method is 86.40% and the Kappa coefficient is 0.80. The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision. However, there are some cases where the finely divided plots are misleading, so further optimization and improvement are needed. Keywords: UAV, visible band, remote sensing, extraction of cultivated land area, object oriented method DOI: 10.25165/j.ijabe.20191203.4754 Citation: Xu W C, Lan Y B, Li Y H, Luo Y F, He Z Y. Classification method of cultivated land based on UAV visible light remote sensing. Int J Agric & Biol Eng, 2019; 12(3): 103–109.References
[1] Guo P, Wu F D, Dai J G, Wang H J, Xu L P, Zhang G S. Comparison of farmland crop classification methods based on visible light images of unmanned aerial vehicles. Transactions of the CSAE, 2017; 33(13): 112–119. (in Chinese)
[2] Zhang J, Feng L, Yao F. Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information. ISPRS Journal of Photogrammetry & Remote Sensing, 2014; 94: 218–223.
[3] Lv T, Liu C. Study on extraction of crop information using time-series MODIS data in Chao Phraya Basin of Thailand. Advances in Space Research, 2010; 45(6): 775–784.
[4] Wang P, Luo X W, Zhou Z Y, Zang Y, Hu L. Key technologies of remote sensing information acquisition based on micro-mini UAV. Transactions of the CSAE, 2014; 30(18): 1–12. (in Chinese)
[5] Bryson M, Reid A, Ramos F, Sukkarieh S. Airborne vision-based mapping and classification of large farmland environments. Journal of Field Robotics, 2010; 27(5): 632–655.
[6] Torres-Sanchez J, Pena J M, de Castro A I, López-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computer and Electronics in Agriculture, 2014; 103(2): 104–113.
[7] Gomez-Candon D, De Castro A I, Lopez-Granados F. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 2014; 15(1): 44–56.
[8] Mesas-Carrascosa F J, Notario-García M D, Meroño J E, de la Orden M S, García-Ferrer A. Validation of measurements of land plot area using UAV imagery. International Journal of Applied Earth Observation and Geoinformation, 2014; 33(1): 270–279.
[9] Wang X Q, Wang M M, Wang S Q, Wu Y D. Extraction of vegetation information based on remote sensing of UAVs in visible band. Transactions of the CSAE, 2015; 31(5): 152–159. (in Chinese)
[10] Liu F, Liu S H, Xiang Y. Study on remote sensing monitoring of vegetation coverage in the field. Transactions of the CSAM, 2014; 45(11): 250–257. (in Chinese)
[11] Dong M, Su J D, Liu G Y, Yang J T, Chen X Z, Tian L, et al. Object oriented remote sensing image of tobacco planting area extraction and monitoring. Science of Surveying and Mapping, 2014; 39(9): 87–90. (in Chinese)
[12] Wu W, Qin Q M, Fan Y D. Information extraction of disaster relief tents based on UAV visible remote sensing image. Geography and Geographic Information Science, 2015; 31(2): 20–23. (in Chinese)
[13] Han W T, Zhang L Y, Zhang H X, Shi Z Q, Yuan M C, Wang Z J. Information extraction of field canal system distribution based on UAV remote sensing and object oriented method. Transactions of the CSAM, 2017; 48(3): 206–214. (in Chinese)
[14] Li Z N, Chen Z X, Wang L M, Liu J, Zhou Q B. Extraction of lodging area of corn based on remote sensing of small UAV. Transactions of the CSAE, 2014; 30(19): 207–213.
[15] Li M, Research on information extraction and subdivision management of county cotton based on RS and GIS - A case study of Xiajin County, Shandong Province. Master's thesis, Tai’an: Shandong Agricultural University, 2012. (in Chinese)
[16] Niu Y X, Zhang L Y, Han W T, Shao G M. Fractional vegetation cover extraction method of winter wheat based on UAV remote sensing and vegetation index. Transactions of the CSAM, 2018; 49(4): 213–221. (in Chinese)
[17] Mesas-Carrascosa F J, Rumbao I C, Torres-Sanchez J, Garcia-Ferrer A, Pena J M, Granados F L. Accurate ortho-mosaicked six-band multispectral UAV images as affected by mission planning for precision agriculture proposes. International journal of remote sensing, 2017; 38: 8–10.
[18] Han W T, Li G, Wan M C, Zhang L Y, Shi Z Q. Study on information extraction method of maize planting based on UAV remote sensing technology. Transactions of the CSAM, 2017; 48(1): 140–147. (in Chinese)
[19] Torres-Sanchez J, Pena J M, de Castro A I, López-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, 2014: 103: 104–113.
[20] Meyer G E, Camargo N J. Verification of color vegetation indices for automated crop image application. Computers and Electronics in Agriculture, 2008; 63: 282–293.
[21] Verrelst J, Schaepman M E, Koetz B, Kneubühler M. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment, 2018; 112(5): 2341 – 2353.
[22] Wu J S, Liu H L, Zhang J S. Object oriented classification method of UAV remote sensing image was used to estimate the rice area in the city. Transactions of the CSAE, 2011; 34(1): 70–77. (in Chinese)
[23] Li Q, Gao X Z, Zhang T, Liu K, Gong J M. Experimental analysis of multilevel remote sensing ground object classification under optimal segmentation scale. Journal of Earth Information Science, 2011; 12(3): 409–417. (in Chinese)
[24] Mesas-Carrascosa F J, Notario-García M D, Meroño J E, de la Orden M S, García-Ferrer A. Validation of measurements of land plot area using UAV imagery. International Journal of Applied Earth Observation and Geoinformation, 2014; 33(1): 270–279.
[25] Du M M, Noguchi Noboru. Monitoring of Wheat Growth Status and Mapping of Wheat Yield's within-Field Spatial Variations Using Color Images Acquired from UAV-camera System. Remote sensing, 2017; 9(3): 1–14.
[26] Meyer George E, Neto Joao Camargo. Verification of color vegetation indices for automated crop imaging applications. Computer and Electronics in Agriculture, 2008; 63(2): 282–293.
[27] Torres-Sanchez J, Pena, J M, De Castro A I, Lopez-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computer and Electronics in Agriculture, 2014; 103: 104–113.
[2] Zhang J, Feng L, Yao F. Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information. ISPRS Journal of Photogrammetry & Remote Sensing, 2014; 94: 218–223.
[3] Lv T, Liu C. Study on extraction of crop information using time-series MODIS data in Chao Phraya Basin of Thailand. Advances in Space Research, 2010; 45(6): 775–784.
[4] Wang P, Luo X W, Zhou Z Y, Zang Y, Hu L. Key technologies of remote sensing information acquisition based on micro-mini UAV. Transactions of the CSAE, 2014; 30(18): 1–12. (in Chinese)
[5] Bryson M, Reid A, Ramos F, Sukkarieh S. Airborne vision-based mapping and classification of large farmland environments. Journal of Field Robotics, 2010; 27(5): 632–655.
[6] Torres-Sanchez J, Pena J M, de Castro A I, López-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computer and Electronics in Agriculture, 2014; 103(2): 104–113.
[7] Gomez-Candon D, De Castro A I, Lopez-Granados F. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 2014; 15(1): 44–56.
[8] Mesas-Carrascosa F J, Notario-García M D, Meroño J E, de la Orden M S, García-Ferrer A. Validation of measurements of land plot area using UAV imagery. International Journal of Applied Earth Observation and Geoinformation, 2014; 33(1): 270–279.
[9] Wang X Q, Wang M M, Wang S Q, Wu Y D. Extraction of vegetation information based on remote sensing of UAVs in visible band. Transactions of the CSAE, 2015; 31(5): 152–159. (in Chinese)
[10] Liu F, Liu S H, Xiang Y. Study on remote sensing monitoring of vegetation coverage in the field. Transactions of the CSAM, 2014; 45(11): 250–257. (in Chinese)
[11] Dong M, Su J D, Liu G Y, Yang J T, Chen X Z, Tian L, et al. Object oriented remote sensing image of tobacco planting area extraction and monitoring. Science of Surveying and Mapping, 2014; 39(9): 87–90. (in Chinese)
[12] Wu W, Qin Q M, Fan Y D. Information extraction of disaster relief tents based on UAV visible remote sensing image. Geography and Geographic Information Science, 2015; 31(2): 20–23. (in Chinese)
[13] Han W T, Zhang L Y, Zhang H X, Shi Z Q, Yuan M C, Wang Z J. Information extraction of field canal system distribution based on UAV remote sensing and object oriented method. Transactions of the CSAM, 2017; 48(3): 206–214. (in Chinese)
[14] Li Z N, Chen Z X, Wang L M, Liu J, Zhou Q B. Extraction of lodging area of corn based on remote sensing of small UAV. Transactions of the CSAE, 2014; 30(19): 207–213.
[15] Li M, Research on information extraction and subdivision management of county cotton based on RS and GIS - A case study of Xiajin County, Shandong Province. Master's thesis, Tai’an: Shandong Agricultural University, 2012. (in Chinese)
[16] Niu Y X, Zhang L Y, Han W T, Shao G M. Fractional vegetation cover extraction method of winter wheat based on UAV remote sensing and vegetation index. Transactions of the CSAM, 2018; 49(4): 213–221. (in Chinese)
[17] Mesas-Carrascosa F J, Rumbao I C, Torres-Sanchez J, Garcia-Ferrer A, Pena J M, Granados F L. Accurate ortho-mosaicked six-band multispectral UAV images as affected by mission planning for precision agriculture proposes. International journal of remote sensing, 2017; 38: 8–10.
[18] Han W T, Li G, Wan M C, Zhang L Y, Shi Z Q. Study on information extraction method of maize planting based on UAV remote sensing technology. Transactions of the CSAM, 2017; 48(1): 140–147. (in Chinese)
[19] Torres-Sanchez J, Pena J M, de Castro A I, López-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, 2014: 103: 104–113.
[20] Meyer G E, Camargo N J. Verification of color vegetation indices for automated crop image application. Computers and Electronics in Agriculture, 2008; 63: 282–293.
[21] Verrelst J, Schaepman M E, Koetz B, Kneubühler M. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment, 2018; 112(5): 2341 – 2353.
[22] Wu J S, Liu H L, Zhang J S. Object oriented classification method of UAV remote sensing image was used to estimate the rice area in the city. Transactions of the CSAE, 2011; 34(1): 70–77. (in Chinese)
[23] Li Q, Gao X Z, Zhang T, Liu K, Gong J M. Experimental analysis of multilevel remote sensing ground object classification under optimal segmentation scale. Journal of Earth Information Science, 2011; 12(3): 409–417. (in Chinese)
[24] Mesas-Carrascosa F J, Notario-García M D, Meroño J E, de la Orden M S, García-Ferrer A. Validation of measurements of land plot area using UAV imagery. International Journal of Applied Earth Observation and Geoinformation, 2014; 33(1): 270–279.
[25] Du M M, Noguchi Noboru. Monitoring of Wheat Growth Status and Mapping of Wheat Yield's within-Field Spatial Variations Using Color Images Acquired from UAV-camera System. Remote sensing, 2017; 9(3): 1–14.
[26] Meyer George E, Neto Joao Camargo. Verification of color vegetation indices for automated crop imaging applications. Computer and Electronics in Agriculture, 2008; 63(2): 282–293.
[27] Torres-Sanchez J, Pena, J M, De Castro A I, Lopez-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computer and Electronics in Agriculture, 2014; 103: 104–113.
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Published
2019-06-05
How to Cite
Xu, W., Lan, Y., Li, Y., Luo, Y., & He, Z. (2019). Classification method of cultivated land based on UAV visible light remote sensing. International Journal of Agricultural and Biological Engineering, 12(3), 103–109. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4754
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Information Technology, Sensors and Control Systems
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