In this study, the utilization of drone images and deep learning to monitor the seagrass habitat, which is important in the marine ecosystem, is evaluated. Two experiments were conducted to compare the effect of image normalization and the performance of deep learning models in semantic segmentation with drone optical images acquired for the alpine habitats in coastal waters. Z-score and Min-Max normalization techniques were used to examine the effect of image normalization, and U-Net, SegNet, PSPNet, and DeepLab v3+ were used to compare the performance of the deep learning models. As a result, Min-Max normalization demonstrated outstanding performance for optical images, and Z-score normalization for black and white images. Regardless of the normalization of the image, the performance of the models was ranked in the order of U-Net, PSPNet, SegNet, and DeepLab v3+. Although the latest model, DeepLab v3+, was expected to have excellent performance, in fact, U-Net, having a relatively simple structure and a small number of parameters, showed the best performance. As the accuracy of semantic results seems to depend on the deep learning models and normalization methods, an experiment to determine an appropriate normalization method and deep learning model should be preceded for the semantic segmentation of high-resolution optical images in coastal waters.

在这项研究中，评估了利用无人机图像和深度学习来监测在海洋生态系统中很重要的海草栖息地。进行了两个实验，将图像归一化的效果和深度学习模型在语义分割中的性能与为沿海水域的高山栖息地获取的无人机光学图像进行比较。Z -score 和 Min-Max 归一化技术用于检查图像归一化的效果，U-Net、SegNet、PSPNet 和 DeepLab v3+ 用于比较深度学习模型的性能。因此，Min-Max 归一化展示了光学图像的出色性能，Z- 黑白图像的分数归一化。无论图像的归一化如何，模型的性能按照 U-Net、PSPNet、SegNet 和 DeepLab v3+ 的顺序排列。虽然最新的模型DeepLab v3+被期望有出色的性能，但实际上，结构相对简单、参数少的U-Net表现出了最好的性能。由于语义结果的准确性似乎取决于深度学习模型和归一化方法，因此应在确定合适的归一化方法和深度学习模型的实验之前进行沿海水域高分辨率光学图像的语义分割。