An Efficient Approach Based on UAV Orthographic Imagery to Map Paddy With Support of Field-Level Canopy Height From Point Cloud Data

Unmanned aerial vehicle technology which is capable of acquiring the centimeter-level orthographic images provides a promising way to map the paddy rice extent. However, crop spectral mixture is still the major that limits the performance of paddy rice identification when using the spectral information alone. In order to eliminate the influence of spectral mixture on crop mapping accuracy, an innovative method was proposed to produce field-level canopy height data by calculating the elevation difference between the vegetated and nonvegetated parcels. We applied support vector machine on four types of datasets: 1) Pixel-based Spectral Features (PSF); 2) PSF and Canopy Height Features; 3) Object-based Spectral Features (OSF); and 4) Object-based Spectral Features and Canopy Height Features (OSCHF). The results showed that the full-class classification accuracy from OSCHF was the highest with an overall accuracy of 94.04% and Kappa of 0.91, which was significantly higher than result using OSF, and the accuracy of PSF was the worst. The OSF could eliminate speckle noise problem to some extent, but grapes and trees were still to some extent misclassified as paddy rice due to similar spectrum. Fortunately, these confusions were effectively avoided by including the canopy height.