Studies on Automatic Image Segmentation Method for Canopy Density Measurement of Chinese Alligator (Alligator sinensis) Habitat
Studies on Automatic Image Segmentation Method for Canopy Density Measurement of Chinese Alligator (Alligator sinensis) Habitat
Ke Sun, Guangwei Fan, Yujie Zhang, Ji Luo, Xiaobing Wu and Tao Pan*
ABSTRACT
In order to develop a rapid measuring method for canopy density of Chinese alligator (Alligator sinensis) habitat, the images of the habitat are captured from 1 meter off the ground. Then, a canopy density calculating method is established based on an adaptive bimodal threshold segmentation algorithm. The accuracy of the proposed method is evaluated and compared with two methods based on Otsu’s algorithm and iterative algorithm, respectively. The results show that the accuracy of the method based on the adaptive bimodal threshold segmentation algorithm is the highest (absolute error: 0.018±0.016) among these three methods. Please note that the accuracy is higher for images captured on cloudy days as compared to the images captured on sunny days. Moreover, the accuracy is highest for the images with low canopy density (absolute error: 0.006±0.004), and is relatively low for the images with high canopy density (absolute error: 0.020±0.016). The adaptive bimodal threshold segmentation method satisfies the accuracy requirements of canopy density of Chinese alligator (Alligator sinensis) habitat.
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