Integration of Convolutional Neural Networks and UAV-Derived DEM for the Automatic Classification of Benthic Habitats in Shallow Water Environments

Benthic habitats are highly complex and diverse ecosystems that are increasingly threatened by human-induced stressors and the impacts of climate change. Therefore, accurate classification and mapping of these marine habitats are essential for effective monitoring and management. In recent years, Unmanned Aerial Vehicles (UAVs) have been increasingly used to expand the spatial coverage of surveys and to produce high-resolution imagery. These images can be processed using photogrammetry-based techniques to generate high-resolution digital elevation models (DEMs) and orthomosaics. In this study, we demonstrate that integrating descriptors extracted from pre-trained Convolutional Neural Networks (CNNs) with geomorphometric attributes derived from DEMs significantly enhances the accuracy of automatic benthic habitat classification. To assess this integration, we analyzed orthomosaics and DEMs generated from UAV imagery across three shallow reef zones along the Red Sea coast of Saudi Arabia. Furthermore, we tested various combinations of feature layers from pre-trained CNNs—including ResNet-50, VGG16, and AlexNet—together with several geomorphometric variables to evaluate classification accuracy. The results showed that features extracted from the ResNet-50 FC1000 layer, when combined with twelve geomorphometric attributes based on curvature, slope, the Topographic Ruggedness Index (TRI), and DEM-derived heights, achieved the highest overall accuracies. Moreover, training a Support Vector Machine (SVM) classifier using both pre-trained ResNet-50 features and geomorphometric variables led to an improvement in overall accuracy of up to 5%, compared to using ResNet-50 features alone. The proposed integration effectively improves the automation and accuracy of benthic habitat mapping processes.