Sea-land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery,Marine Geodesy

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Sea-land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery
Marine Geodesy ( IF 1.6 ) Pub Date : 2020-01-20 , DOI: 10.1080/01490419.2020.1713266
Ting Yang ₁ , Shenlu Jiang ₂ , Zhonghua Hong _{1,

3,

4,

5} , Yun Zhang ₁ , Yanling Han ₁ , Ruyan Zhou ₁ , Jing Wang ₁ , Shuhu Yang ₁ , Xiaohua Tong ₄ , Tae-yong Kuc ₂

Affiliation

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 km2 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.

更新日期：2020-01-20

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