LudVision -- Remote Detection of Exotic Invasive Aquatic Floral Species using Drone-Mounted Multispectral Data

TL;DR

This paper presents LudVision, a drone-based multispectral imaging method for detecting invasive aquatic plant species, specifically Ludwigia peploides, achieving high accuracy in remote ecosystem monitoring.

Contribution

It introduces a novel multispectral detection approach using modified semantic segmentation techniques and a new dataset for invasive aquatic species identification.

Findings

Achieved 79.9% producer's accuracy

Achieved 95.5% user's accuracy

Developed a new multispectral dataset for invasive species

Abstract

Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. Ever-growing reports of invasive species have affected the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can negatively impact the surrounding ecosystem and human activities such as agriculture, fishing, and navigation. Our goal was to develop a method to identify the presence of the species. We used images collected by a drone-mounted multispectral sensor to achieve this, creating our LudVision data set. To identify the targeted species on the collected images, we propose a new method for detecting Ludwigia p. in multispectral images. The method is based on existing state-of-the-art semantic segmentation methods modified to handle multispectral data. The proposed method achieved a producer's accuracy of 79.9% and a user's accuracy of 95.5%.