Fig. 1. Description of UAV and camera used for experiment
Fig. 2. Experimental area
Fig. 3. Workflow of RF algorithm
Fig. 5. Example of cropland and grass area
Fig. 6. GLCM result by study area
Fig. 7. Example of ground truth data
Fig. 8. Classification results according to window size of GLCM image
Fig. 9. Detection results of cropland by classification according to window size of GLCM
Fig. 10. 1st Detailed images of detection results by classification according to window size of GLCM
Fig. 11. 2nd Detailed images of detection results by classification according to window size of GLCM
Fig. 4. Example of NDWI image by UAV
Table 1. Specification of eBee and multiSPEC 4C
Table 2. Confusion matrix of classification results using multispectral image
Table 3. Confusion matrix of classification results using multispectral, NDVI and NDWI images
Table 4. Confusion matrix of classification results using multispectral, NDVI, NDWI and GLCM images (window size of GLCM : 3)
Table 5. Confusion matrix of classification results using multispectral, NDVI, NDWI and GLCM images (window size of GLCM : 5)
Table 6. Confusion matrix of classification results using multispectral, NDVI, NDWI and GLCM images (window size of GLCM : 7)
Table 7. Confusion matrix of classification results using multispectral, NDVI, NDWI and GLCM images (window size of GLCM : 15)
Table 8. Confusion matrix of classification results using multispectral, NDVI, NDWI and GLCM images (window size of GLCM : 31)
Table 9. Confusion matrix of classification results using multispectral, NDVI, NDWI and GLCM images (window size of GLCM : 63)
참고문헌
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피인용 문헌
- Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables vol.12, pp.12, 2018, https://doi.org/10.3390/rs12122049