Individual tree species classification based on terrestrial laser scanning using curvature estimation and convolutional neural network

9Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

In this paper, we propose a new method for specifying individual tree species based on depth and curvature image creation from point cloud captured by terrestrial laser scanner and Convolutional Neural Network (CNN). Given a point cloud of an individual tree, the proposed method first extracts the subset of points corresponding to a trunk at breast-height. Then branches and leaves are removed from the extracted points by RANSAC-based circle fitting, and the depth image is created by globally fitting a cubic polynomial surface to the remaining trunk points. Furthermore, principal curvatures are estimated at each scanned point by locally fitting a quadratic surface to its neighbouring points. Depth images clearly capture the bark texture involved by its split and tear-off, but its computation is unstable and may fail to acquire bark shape in the resulting images. In contrast, curvature estimation enables stable computation of surface concavity and convexity, and thus it can well represent local geometry of bark texture in the curvature images. In comparison to the depth image, the curvature image enables accurate classification for slanted trees with many branches and leaves. We also evaluated the effectiveness of a multi-modal approach for species classification in which depth and curvature images are analysed together using CNN and support vector machine. We verified the superior performance of our proposed method for point cloud of Japanese cedar and cypress trees.

Cite

CITATION STYLE

APA

Mizoguchi, T., Ishii, A., & Nakamura, H. (2019). Individual tree species classification based on terrestrial laser scanning using curvature estimation and convolutional neural network. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 1077–1082). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-2-W13-1077-2019

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free