Investigating the efficiency and capabilities of UAVs and Convolutional Neural Networks in the field of remote sensing as a land classification tool

Abstract

The study aimed to determine the efficacy and capabilities of using high-resolution aerial imagery and a convolutional neural network (CNN) to identify plant species and monitor land cover and land change in the context of remote sensing. The full capabilities of a CNN were examined including testing whether the platform could be used for land cover and the evaluation of land change over time. An unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area. The CNN was coded and operated in RStudio, and digitised data from the input imagery were used as training and validation data by the programme to learn features, and thereafter classification of the Opuntia invasive plant species was performed. Accuracy assessments were done on the results to test efficacy through reliability and accuracy. The classification achieved an overall accuracy of 93%, and the kappa coefficient score was 0.86. CNN was also able to predict the land coverage area of Opuntia to be within 4% of the ground truthing data. A change in land cover over time was detected by the programme after the manual clearing of the plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications and that it can be used for monitoring land cover by accurately estimating the spatial distribution of plant species and by monitoring the species' growth or decline over time and is therefore efficient methodology and its uses in remote sensing could be expanded.