A segmentation network with multiattention and its application to SAR image analysis. Issue 4 (13th January 2020)

• Record Type: Journal Article
• Authors: An, Yong; Long, Jianwu; Mabu, Shingo
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 15:Issue 4(2020)
• Page Start: 570
• Page End: 576
• Publication Date: 2020-01-13
• Subjects: semantic segmentation -- deep learning -- attention model -- remote sensing -- SAR image; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.23090
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract: Image segmentation plays an important role in image understanding and region‐based applications. Many image segmentation algorithms have been proposed, but in this paper, we enhance the segmentation performance of deep learning using attention models that extract important features from the target images. The structure of the segmentation network is an encoder–decoder model that can combine position features and channel features, where the attention mechanisms refine both position and channel features. In the experiments, the proposed method is applied to satellite image analysis, where synthetic aperture radar images are analyzed to detect landslide areas after heavy rain occurred. The experimental results show that the proposed method obtains higher segmentation accuracy comparing with some conventional methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.