Study on the extraction method of sugar tangerine fruit trees based on UAV remote sensing images

Objective  To obtain remote sensing image of sand sugar tangerine orchard by UAV, rapidly extract the distribution position of fruit trees, and provide references for growth monitoring and yield prediction of fruit trees.

Method  The visible light remote sensing images taken by drones were used as the research object. Six visible light vegetation indexes of excess red index, excess green index, excess blue index, visible band differential vegetation index, red-green ratio index and blue-green ratio index were calculated. We used the double peak threshold method to select the threshold for fruit tree extraction. Based on the spectral index identification, digital surface model was added as input variable of the identification model, and the comparative test was conducted.

Result  Compared with using a single spectral index, the addition of digital surface model improved the extraction accuracies of fruit tree and non fruit tree pixels. The total accuracies of six band fusions were all greater than 97%. The total accuracy of excess red index combined with digital surface model was the highest (98.77%) with Kappa coefficient of 0.956 7, and the vegetation extraction accuracies were superior to those of other five combinations of visible light vegetation indexes with digital surface model.

Conclusion  The combination of digital surface model with visible light vegetation index can excavate more deeply the information contained in the remote sensing data, and provide a reference for the extraction of similar tonal features in the image.