Sea-Land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery. Issue 2 (3rd March 2020)

• Record Type: Journal Article
• Authors: Yang, Ting; Jiang, Shenlu; Hong, Zhonghua; Zhang, Yun; Han, Yanling; Zhou, Ruyan; Wang, Jing; Yang, Shuhu; Tong, Xiaohua; Kuc, Tae-yong
• Is Part Of: Marine geodesy. Volume 43:Issue 2(2020)
• Page Start: 105
• Page End: 133
• Publication Date: 2020-03-03
• Subjects: Deep learning -- Landsat-8 OLI imagery -- model selection -- sea-land segmentation; Marine geodesy -- Periodicals; Hydrographic surveying -- Periodicals; 526.99
• DOI: 10.1080/01490419.2020.1713266
• Languages: English
• ISSNs: 0149-0419

Abstract

Abstract: 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 .