Version 1
: Received: 8 July 2024 / Approved: 9 July 2024 / Online: 9 July 2024 (05:45:25 CEST)
How to cite:
LEE, S. J.; Yoon, H. S.; Kwak, S. W. Attention Semantic Segmentation Network for Remote Sensing Images of Water Area. Preprints2024, 2024070702. https://doi.org/10.20944/preprints202407.0702.v1
LEE, S. J.; Yoon, H. S.; Kwak, S. W. Attention Semantic Segmentation Network for Remote Sensing Images of Water Area. Preprints 2024, 2024070702. https://doi.org/10.20944/preprints202407.0702.v1
LEE, S. J.; Yoon, H. S.; Kwak, S. W. Attention Semantic Segmentation Network for Remote Sensing Images of Water Area. Preprints2024, 2024070702. https://doi.org/10.20944/preprints202407.0702.v1
APA Style
LEE, S. J., Yoon, H. S., & Kwak, S. W. (2024). Attention Semantic Segmentation Network for Remote Sensing Images of Water Area. Preprints. https://doi.org/10.20944/preprints202407.0702.v1
Chicago/Turabian Style
LEE, S. J., Hong Sik Yoon and Sang Woo Kwak. 2024 "Attention Semantic Segmentation Network for Remote Sensing Images of Water Area" Preprints. https://doi.org/10.20944/preprints202407.0702.v1
Abstract
With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.
Keywords
Remote sensing; semantic segmentation; deep convolutional neural networks
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
