U-Net Performance for Beach Wrack Segmentation: Effects of UAV Camera Bands, Height Measurements, and Spectral Indices

Abstract

This study delves into the application of the U-Net convolutional neural network (CNN) model for beach wrack (BW) segmentation and monitoring in coastal environments using multispectral imagery. Through the utilization of different input configurations, namely, “RGB”, “RGB and height”, “5 bands”, “5 bands and height”, and “Band ratio indices”, this research provides insights into the optimal dataset combination for the U-Net model. The results indicate promising performance with the “RGB” combination, achieving a moderate Intersection over Union (IoU) of 0.42 for BW and an overall accuracy of IoU = 0.59. However, challenges arise in the segmentation of potential BW, primarily attributed to the dynamics of light in aquatic environments. Factors such as sun glint, wave patterns, and turbidity also influenced model accuracy. Contrary to the hypothesis, integrating all spectral bands did not enhance the model’s efficacy, and adding height data acquired from UAVs decreased model precision in both RGB and multispectral scenarios. This study reaffirms the potential of U-Net CNNs for BW detection, emphasizing the suitability of the suggested method for deployment in diverse beach geomorphology, requiring no high-end computing resources, and thereby facilitating more accessible applications in coastal monitoring and management.

Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans

“…U-net networks, which look like U’s, are full convolutional networks optimized by fully convolutional networks 26 . The U-type networks need a smaller dataset size and boast higher segmentation accuracy than other convolutional neural networks 27 . The vanilla U-net network compromises a down-sampling path (encoder) in the left of the network and an up-sampling path (decoder) in the right.…”