Improved Sea-Ice Identification Using Semantic Segmentation With Raindrop Removal

Alsharay, Nahed M. and Chen, Yuanzhu and Dobre, Octavia A. and De Silva, Oscar (2022) Improved Sea-Ice Identification Using Semantic Segmentation With Raindrop Removal. IEEE Access, 10. ISSN 2169-3536

[img] [English] PDF - Published Version
Available under License Creative Commons Attribution Non-commercial.

Download (2MB)


Sea-ice identification is an essential process for safety critical navigation support of surface vessels in polar waters. Semantic segmentation has drawn much attention as an enabling technique for fast detection of objects in a scene including sea-ice conditions. Identifying sea-ice is a challenging problem, especially in the presence of raindrops. The raindrop alters the boundaries of the objects in the scene, and thus, degrades the identification performance. In this work, a raindrop removing framework is developed to enhance the classification performance. Three deep-learning semantic segmentation networks are trained to classify the scene of sea-ice images into ice, water, ship, and sky. The deep-learning networks are VGG-16, fully convolutional network, and pyramid scene parsing network. Transfer learning along with data augmentation operations have been implemented to improve the training process. Results illustrate that data augmentation operations enhance the performance of the three models. Moreover, the raindrop removing framework improves the models’ performance, e.g. the average intersection over union of the VGG-16 model is improved from 85.91% to 91.70%.

Item Type: Article
Item ID: 15545
Additional Information: Memorial University Open Access Author's Fund
Keywords: Convolutional neural networks, raindrop removing, sea-ice, semantic segmentation
Department(s): Science, Faculty of > Computer Science
Date: 10 February 2022
Date Type: Publication
Digital Object Identifier (DOI):
Related URLs:

Actions (login required)

View Item View Item


Downloads per month over the past year

View more statistics