Improved FCM algorithm for fisheye image cluster analysis for tree height calculation

Abstract

The height of standing trees is an important index in forestry research. This index is not only hard to measure directly but also the environmental factors increase the measurement difficulty. Therefore, the measurement of the height of standing trees is always a problem that experts and scholars are trying to improve. In this study, improve fuzzy c-means algorithm to reduce the calculation time and improve the clustering effect, used on this image segmentation technology, a highly robust non-contact measuring method for the height of standing trees was proposed which is based on a smartphone with a fisheye lens. While ensuring the measurement accuracy, the measurement stability is improved. This method is simple to operate, just need to take a picture of the standing tree and determine the shooting distance to complete the measurement. The purpose of the fisheye lens is to ensure that the tree remains intact in the photograph and to reduce the shooting distance. The results of different stability experiments show that the measurement error ranged from -0.196m to 0.195m, and the highest relative error of tree measurement was 3.05%, and the average relative error was 1.45%. Analysis shows that compared with previous research, this method performs better at all stages. The proposed approach can provide a new way to obtain tree height, which can be used to analyze growing status and change in contrast height because of high accuracy and permanent preservation of images.

Keywords

• tree height estimation
• fcm
• image segmentation
• fisheye image
• photogrammetry