research-article
Authors: Guo Yanjun, Du Yunyan, Yan Ming, Xie Ting, Liu Moyun, Wang Nan
Volume 232, Issue C
Published: 30 April 2025 Publication History
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Abstract
This paper introduces an innovative deep convolutional neural network, the wave-shaped CNN, tailored for the robust extraction of a marine-raft aquaculture area (MRAA) from synthetic-aperture-radar (SAR) images. Confronting the intricate challenges posed by coherent noise, environmental variability, and the distinction between rafting areas and their background within SAR images, the proposed wave-shaped CNN provides a novel solution for dynamic MRAA monitoring, which collaboratively incorporates the feature-attention subnetwork (FAS) and feature-refinement subnetwork (FRS) with residual connections to refine the feature-extraction process. Specifically, the FAS can adeptly extract both comprehensive global and nuanced local features from the multi-scale characteristics of SAR images. Furthermore, the FRS introduces a series of N-shaped subnetworks aimed at addressing the prevalent issue of edge adhesion observed within SAR images. In addition, a specialized SAR-MRAA dataset is developed, which allows for enriching the resource for the MRAA task in SAR images. Comprehensive experimental analyses conducted on the proposed wave-shaped CNN show its superior performance and effectiveness in MRAA extraction, demonstrating its advantages in establishing baselines within this domain. The code is available at https://github/gmy63000/Wave-shaped-CNN.
Highlights
•
A wave-shaped CNN is designed to detect marine-raft aquaculture area in SAR image.
•
A FAS is used to extract both global and local features effectively from SAR image.
•
A FRS is proposed to address the problem of edge adhesion observed in SAR image.
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A SAR-MRAA dataset was built to facilitate the research of this area in this work.
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Index Terms
A wave-shaped CNN for marine-raft aquaculture-area extraction in SAR images
Applied computing
Computers in other domains
Agriculture
Computing methodologies
Artificial intelligence
Computer vision
Computer vision problems
Object detection
Computer vision representations
Appearance and texture representations
Machine learning
Machine learning approaches
Neural networks
Index terms have been assigned to the content through auto-classification.
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Information
Published In
Computers and Electronics in Agriculture Volume 232, Issue C
May 2025
1311 pages
Issue’s Table of Contents
Elsevier B.V.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 30 April 2025
Author Tags
- Remote sensing
- Synthetic aperture radar (SAR) images
- Marine-floating-raft aquaculture
- Deep learning
- Convolutional neural network (CNN)
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- Research-article
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