A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images
In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultu...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/1860160 |
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| author | Huaming Xie Qianjiao Wu Ting Zhang Zhende Teng Hao Huang Ying Shu Shaoru Feng Jing Lou |
| author_facet | Huaming Xie Qianjiao Wu Ting Zhang Zhende Teng Hao Huang Ying Shu Shaoru Feng Jing Lou |
| author_sort | Huaming Xie |
| collection | DOAJ |
| description | In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production. It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies. Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy. Initially, the segmentation of the image is carried out. To this end, the parcel data is used to generate the region of interest (ROI) of each parcel. Second, the homogeneity of each ROI is detected by its statistical indices (mean value and standard deviation). Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous ROIs. Finally, two classification results are combined as the final classification result. The new algorithm was applied to a complex planting area of 103.60 km2 in central China based on the ownership parcel data and Gaofen-1 PMS and WFV remote sensing images in this paper. The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa = 0.9279, overall accuracy = 96.41%, user’s accuracy = 99.16%, producer’s accuracy = 93.39%, commission errors = 0.84%, and omission errors = 6.61%) when the size of ownership parcels matches the spatial resolution of remote sensing images. |
| format | Article |
| id | doaj-art-82090f78b0ec43268d87691ead79225e |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-82090f78b0ec43268d87691ead79225e2025-02-03T07:24:16ZengWileyJournal of Mathematics2314-47852021-01-01202110.1155/2021/1860160A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing ImagesHuaming Xie0Qianjiao Wu1Ting Zhang2Zhende Teng3Hao Huang4Ying Shu5Shaoru Feng6Jing Lou7School of Environment and Energy EngineeringInstitute of Remote Sensing and Geographic Information SystemsSchool of Environment and Energy EngineeringSchool of Environment and Energy EngineeringSchool of Environment and Energy EngineeringSchool of Environment and Energy EngineeringSchool of Environment and Energy EngineeringRemote Sensing Application CenterIn the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production. It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies. Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy. Initially, the segmentation of the image is carried out. To this end, the parcel data is used to generate the region of interest (ROI) of each parcel. Second, the homogeneity of each ROI is detected by its statistical indices (mean value and standard deviation). Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous ROIs. Finally, two classification results are combined as the final classification result. The new algorithm was applied to a complex planting area of 103.60 km2 in central China based on the ownership parcel data and Gaofen-1 PMS and WFV remote sensing images in this paper. The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa = 0.9279, overall accuracy = 96.41%, user’s accuracy = 99.16%, producer’s accuracy = 93.39%, commission errors = 0.84%, and omission errors = 6.61%) when the size of ownership parcels matches the spatial resolution of remote sensing images.http://dx.doi.org/10.1155/2021/1860160 |
| spellingShingle | Huaming Xie Qianjiao Wu Ting Zhang Zhende Teng Hao Huang Ying Shu Shaoru Feng Jing Lou A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images Journal of Mathematics |
| title | A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images |
| title_full | A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images |
| title_fullStr | A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images |
| title_full_unstemmed | A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images |
| title_short | A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images |
| title_sort | new algorithm for extracting winter wheat planting area based on ownership parcel vector data and medium resolution remote sensing images |
| url | http://dx.doi.org/10.1155/2021/1860160 |
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