Fast Treetops Counting Using Mathematical Image Symmetry, Segmentation, and Fast k-Means Classification Algorithms

Abstract

Forests are important because they contribute to biodiversity, regulation of carbon dioxide, protection of hydrographic basins, wood production. This article presents a methodology for fast and effective counting of treetops using the mathematical symmetry of the grayscale image. For the treetop counting, the unsupervised k-means classification Algorithm was used with two groups or centroids: treetop and not-treetop. By using these groups and the mathematical symmetry of the image, a fast k-means classification Algorithm is generated. To solve the problem of treetop overlapping and perform a more accurate counting, the watershed Algorithm was used. This methodology has a mean treetop count accuracy of 98.3% with a confidence level of 99% in the interval (97.31, 99.7). Aerial images of the coniferous forest of Alcudia, Mallorca, Spain were used. Forests attenuate climatic changes originated by global warming. Drastic climatic changes cause catastrophes to humanity. This research would help the automatic, massive and recurring counting of treetops with the aim of obtaining forest inventories in order to take care of forests.