Comparison of Image Segmentation Methods Based on Digital Hemisphere Photography

According to reresearch, the corner detection-based threshold segmentation algorithm has been found to outperform the Otsu method in handling mixed pixels in canopy imaging. This paper aims to describe the principle of the corner detection-based threshold segmentation algorithm and compare its classification performance to that of the Otsu method through experiments. The results indicate that compared to the Otsu method, the corner detection-based threshold segmentation algorithm achieves higher accuracy in classifying mixed pixels, preserves more canopy information in overexposed areas of the image, effectively reduces the misclassification of mixed pixels, and greatly improves the accuracy of hemispherical photography leaf area index inversion results. Specifically, the correlation coefficient R
²
of the corner detection-based threshold segmentation algorithm is shown to increase from 0.8 to 0.9, demonstrating its superior performance.