Sea-Land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery

• Published in: Marine geodesy 2020-03, Vol.43 (2), p.105-133
• Main Authors: Yang, Ting, Jiang, Shenlu, Hong, Zhonghua, Zhang, Yun, Han, Yanling, Zhou, Ruyan, Wang, Jing, Yang, Shuhu, Tong, Xiaohua, Kuc, Tae-yong
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Bayesian analysis; Benchmarks; Coasts; Complexity; Criteria; Datasets; Deep learning; Feasibility studies; Image processing; Image segmentation; Imagery; Landsat; Landsat satellites; Landsat-8 OLI imagery; Machine learning; Mapping; Methods; model selection; Neural networks; Probability theory; Remote sensing; Satellite imagery; sea-land segmentation; Semantic segmentation; Semantics; Stability; Stability analysis; Training
• ISSN: 0149-0419; 1521-060X
• DOI: 10.1080/01490419.2020.1713266

Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural networks (DCNNs) have performed well in semantic segmentation in recent years. However, sea-land segmentation using deep learning techniques remains a challenging task, due to the lack of a benchmark dataset and the difficulty of deciding which semantic segmentation model to use. We present a comparative framework of sea-land segmentation to Landsat-8 OLI imagery via semantic segmentation in deep learning techniques. Three issues are investigated: (1) constructing a sea-land benchmark dataset using Landsat-8 Operational Land Imager (OLI) imagery consisting of 18,000 km 2 of coastline around China; (2) evaluating the feasibility and performance of sea-land segmentation by comparing the accuracy assessment, time complexity, spatial complexity and stability of state-of-the-art DCNNs methods; (3) choosing the most suitable semantic segmentation model for sea-land segmentation in accordance with Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection. Results show that the average test accuracy achieves over 99% accuracy, and the mean Intersection over Unions (mean IoU) is above 92%. These findings demonstrate that the Fully Convolutional DenseNet (FC-DenseNet) performs better than other state-of-the-art methods in sea-land segmentation, based on both AIC and BIC. Considering training time efficiency, DeeplabV3+ performs better for sea-land segmentation. The sea-land segmentation benchmark dataset is available at: https://pan.baidu.com/s/1BlnHiltOLbLKe4TG8lZ5xg .