MultiSpecies Canopy Segmentation: Interactive Machine-Learning and Pseudo-Labelling are key

Abstract

This study investigates the challenge of creating datasets for training multiclass deep-learning segmentation models, specifically for segmenting multi-species canopy images. Creating training sets for deep-learning based segmentation of multispecies canopies is currently too labor-intensive and time-consuming to be viable. To address this challenge, we propose a novel pipeline that uses fully convolutional neural networks (FCNNs) to transition from single-species images to segmented multi-species images. This paper demonstrates that FCNNs can effectively generalize learning from single-species canopy images to multispecies canopy images, achieving accurate pixel classification in mixed species canopies even when the network was trained only on images of single-species canopies. Additionally, we introduce Interactive Machine Learning and pseudo labeling as a method for generating a single-species canopy training set in a matter of minutes. We also present two software packages to implement our approach and extensively evaluate them against several baselines. Our findings demonstrate that our approach can significantly reduce the human time load required for semantic segmentation of multispecies canopy images, achieving over 90% accuracy in less than 10 minutes. This new method has the potential to greatly facilitate the study of multispecies canopies.