An Automatic Segmentation Method Based On Geometrical Features In Hemispherical Images For Forest Management

This work presents an automatic pixel-based approach for image segmentation in hemispherical images obtained by a stereoscopic imaging device with fish-eye lenses for forest inventories purposes. In this way, the objective of the proposed image segmentation process is the identification of tree stems. To that end, four geometrical features were obtained for each pixel taken into account the intensity, the local color variance in both radial and tangential directions, and the greenness ratio. Thus, the proposed approach is an automated method based on a global image classification based on the four aforementioned features followed by a constrained region growing process. As a result, each pixel is classified as belonging to Sky, Foliage and Stem texture classes. The quality of the segmented images was successfully evaluated against two segmentation strategies previously used for similar purposes, by using three well-known image quality metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index Metric (FSIM). The results obtained in this work confirm the relevance of this automatic proposal.