Semantic Segmentation of Drone Imagery Using Deep Learning for Seagrass Habitat Monitoring

A seagrass that is marine vascular plants plays an important role in the marine ecosystem, so periodic monitoring of seagrass habitats is being performed. Recently, the use of drones that can easily acquire very high-resolution imagery is increasing to efficiently monitor seagrass habitats. And deep learning based on a convolutional neural network has shown excellent performance in semantic segmentation. So, studies applied to deep learning models have been actively conducted in remote sensing. However, the segmentation accuracy was different due to the hyperparameter, various deep learning models and imagery. And the normalization of the image and the tile and batch size are also not standardized. So, seagrass habitats were segmented from drone-borne imagery using a deep learning that shows excellent performance in this study. And it compared and analyzed the results focused on normalization and tile size. For comparison of the results according to the normalization, tile and batch size, a grayscale image and grayscale imagery converted to Z-score and Min-Max normalization methods were used. And the tile size is increased at a specific interval while the batch size is allowed the memory size to be used as much as possible. As a result, IoU was 0.26 similar to 0.4 higher than that of Z-score normalized imagery than other imagery. Also, it was found that the difference to 0.09 depending on the tile and batch size. The results were different according to the normalization, tile and batch. Therefore, this experiment found that these factors should have a suitable decision process.