A High-Precision Crop Classification Method Based on Time-Series UAV Images
Timely and accurate information on crop planting structures is crucial for ensuring national food security and formulating economic policies. This study presents a method for high-precision crop classification using time-series UAV (unmanned aerial vehicle) images. Before constructing the time-serie...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/13/1/97 |
| _version_ | 1797447294888443904 |
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| author | Quan Xu Mengting Jin Peng Guo |
| author_facet | Quan Xu Mengting Jin Peng Guo |
| author_sort | Quan Xu |
| collection | DOAJ |
| description | Timely and accurate information on crop planting structures is crucial for ensuring national food security and formulating economic policies. This study presents a method for high-precision crop classification using time-series UAV (unmanned aerial vehicle) images. Before constructing the time-series UAV images, Euclidian distance (ED) was utilized to calculate the separability of samples under various vegetation indices. Second, co-occurrence measures and the gray-level co-occurrence matrix (GLCM) were employed to derive texture characteristics, and the spectral and texture features of the crops were successfully fused. Finally, random forest (RF) and other algorithms were utilized to classify crops, and the confusion matrix was applied to assess the accuracy. The experimental results indicate the following: (1) Time-series UAV remote sensing images considerably increased the accuracy of crop classification. Compared to a single-period image, the overall accuracy and kappa coefficient increased by 26.65% and 0.3496, respectively. (2) The object-oriented classification method was better suited for the precise classification of crops. The overall accuracy and kappa coefficient increased by 3.13% and 0.0419, respectively, as compared to the pixel-based classification results. (3) RF obtained the highest overall accuracy and kappa coefficient in both pixel-based and object-oriented crop classification. RF’s producer accuracy and user accuracy for cotton, spring wheat, cocozelle, and corn in the study area were both more than 92%. These results provide a reference for crop area statistics and agricultural precision management. |
| first_indexed | 2024-03-09T13:53:50Z |
| format | Article |
| id | doaj.art-3176fc5512ab49e78c0dd31d0225638f |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T13:53:50Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-3176fc5512ab49e78c0dd31d0225638f2023-11-30T20:45:42ZengMDPI AGAgriculture2077-04722022-12-011319710.3390/agriculture13010097A High-Precision Crop Classification Method Based on Time-Series UAV ImagesQuan Xu0Mengting Jin1Peng Guo2Urumqi Natural Resources Comprehensive Survey Center, China Geological Survey, Urumqi 830057, ChinaUrumqi Natural Resources Comprehensive Survey Center, China Geological Survey, Urumqi 830057, ChinaDepartment of Tourism and Geography, College of Science, Shihezi University, Shihezi 832003, ChinaTimely and accurate information on crop planting structures is crucial for ensuring national food security and formulating economic policies. This study presents a method for high-precision crop classification using time-series UAV (unmanned aerial vehicle) images. Before constructing the time-series UAV images, Euclidian distance (ED) was utilized to calculate the separability of samples under various vegetation indices. Second, co-occurrence measures and the gray-level co-occurrence matrix (GLCM) were employed to derive texture characteristics, and the spectral and texture features of the crops were successfully fused. Finally, random forest (RF) and other algorithms were utilized to classify crops, and the confusion matrix was applied to assess the accuracy. The experimental results indicate the following: (1) Time-series UAV remote sensing images considerably increased the accuracy of crop classification. Compared to a single-period image, the overall accuracy and kappa coefficient increased by 26.65% and 0.3496, respectively. (2) The object-oriented classification method was better suited for the precise classification of crops. The overall accuracy and kappa coefficient increased by 3.13% and 0.0419, respectively, as compared to the pixel-based classification results. (3) RF obtained the highest overall accuracy and kappa coefficient in both pixel-based and object-oriented crop classification. RF’s producer accuracy and user accuracy for cotton, spring wheat, cocozelle, and corn in the study area were both more than 92%. These results provide a reference for crop area statistics and agricultural precision management.https://www.mdpi.com/2077-0472/13/1/97UAVtime-seriesobject-orientedcropclassification |
| spellingShingle | Quan Xu Mengting Jin Peng Guo A High-Precision Crop Classification Method Based on Time-Series UAV Images Agriculture UAV time-series object-oriented crop classification |
| title | A High-Precision Crop Classification Method Based on Time-Series UAV Images |
| title_full | A High-Precision Crop Classification Method Based on Time-Series UAV Images |
| title_fullStr | A High-Precision Crop Classification Method Based on Time-Series UAV Images |
| title_full_unstemmed | A High-Precision Crop Classification Method Based on Time-Series UAV Images |
| title_short | A High-Precision Crop Classification Method Based on Time-Series UAV Images |
| title_sort | high precision crop classification method based on time series uav images |
| topic | UAV time-series object-oriented crop classification |
| url | https://www.mdpi.com/2077-0472/13/1/97 |
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