A screening approach for the correction of distortion in UAV data for coral community mapping

Abstract

Unmanned Aerial Vehicle (UAV) may provide us super resolution data, however, they are often captured with artefacts and distortion. To investigate impact of distortion on the coral reef information, UAV surveys were deployed using multispectral sensor over two reefs in Bidong Island (Malaysia). Band-specific analysis of distortion revealed five different types of distorted images from the acquisition. This study optimized screening distorted images by comparing the seven distortion correction approaches and validated coral classification maps based on machine learning algorithms [support vector machine (SVM), random forest (RF) and artificial neural network (ANN)]. Results indicate that the screening the green band (b2) alone or the blue band (b1) combined with b2 of UAV data and SVM capable of generating the best coral classification maps, with an overall accuracy of 7–17% improved compared to that of orthomosaic without distortion correction. The proposed distortion correction method can be applied to similar coral environments. Highlights Five different types of artefacts and distortions found in UAV data. A optimized screening approach suggested to minimized image distortion. Distortion corrected data and SVM algorithm performed the best in coral habitat mapping. An overall accuracy of 7–17% improved compared to that of distortion uncorrected maps. A new photogrammetric contribution to the existing automatic orthorectification techniques.