A multilayer feedforward neural network for automatic classification of eucalyptus forests in airborne video imagery

Abstract

Eucalypt tree dieback is a disease that threatens the survival of woodlands in Australian national parks. For mapping and monitoring the spatial distribution of dieback, airborne imaging technologies can be more effective than ground surveys. Amongst the numerous types of airborne sensors, the video camera provides images with very high spatial resolution. In order to detect individual defoliated Eucalyptus trees at Mt Eccles national park (south‐western Victoria), aerial video data was acquired across the study site. Highlighting the health status of sparse and mainly unclustered defoliated eucalypts at Mt Eccles through video images was deemed to be achievable in several steps. This paper introduces a classification method based on a feedforward neural network, whose main goal is to perform a segmentation of the video frames into three classes, namely, bare branches or trunks, healthy canopy and understorey vegetation. The aim of the algorithm is to create a subset of the eucalypt tree group, including defoliated and dead trees, for further analysis. The results suggest that the recognition of trunks and systems of bare branches is feasible using the neural network architecture. This provides a means to pre‐process the video data so as to analyse the health of trees and thus assist park managers with managing dieback.