Automatic Extraction Method of Aquaculture Sea Based on Improved SegNet Model

Xie, Weiyi; Ding, Yuan; Rui, Xiaoping; Zou, Yarong; Zhan, Yating

doi:10.3390/w15203610

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…The CBAM module is able to derive the attention map sequentially along the two independent dimensions of channel and space with a very small amount of computation, carry out adaptive feature refinement, and suppress the noise interference, so as to realize adaptive extraction of water body features [31]. The structure of the CBAM module is shown in Figure 5.…”

Section: Convolutional Block Attention Module (Cbam)mentioning

confidence: 99%

A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images

Liu,

2024

Applied Sciences

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Extraction of lake water bodies from remote sensing images provides reliable data support for water resource management, environmental protection, natural disaster early warning, and scientific research, and helps to promote sustainable development, protect the ecological environment and human health. With reference to the classical encoding-decoding semantic segmentation network, we propose the network model R50A3-LWBENet for lake water body extraction from remote sensing images based on ResNet50 and three attention mechanisms. R50A3-LWBENet model uses ResNet50 for feature extraction, also known as encoding, and squeeze and excitation (SE) block is added to the residual module, which highlights the deeper features of the water body part of the feature map during the down-sampling process, and also takes into account the importance of the feature map channels, which can better capture the multiscale relationship between pixels. After the feature extraction is completed, the convolutional block attention module (CBAM) is added to give the model a global adaptive perception capability and pay more attention to the water body part of the image. The feature map is up-sampled using bilinear interpolation, and the features at different levels are fused, a process also known as decoding, to finalize the extraction of the lake water body. Compared with U-Net, AU-Net, RU-Net, ARU-Net, SER34AUNet, and MU-Net, the R50A3-LWBENet model has the fastest convergence speed and the highest MIoU accuracy with a value of 97.6%, which is able to better combine global and local information, refine the edge contours of the lake’s water body, and have stronger feature extraction capability and segmentation performance.

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Section: Convolutional Block Attention Module (Cbam)mentioning

confidence: 99%

A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images

Liu,

2024

Applied Sciences

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DEUFormer: High‐precision semantic segmentation for urban remote sensing images

Jia,

Song,

Rao

et al. 2024

IET Computer Vision

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Urban remote sensing image semantic segmentation has a wide range of applications, such as urban planning, resource exploration, intelligent transportation, and other scenarios. Although UNetFormer performs well by introducing the self‐attention mechanism of Transformer, it still faces challenges arising from relatively low segmentation accuracy and significant edge segmentation errors. To this end, this paper proposes DEUFormer by employing a special weighted sum method to fuse the features of the encoder and the decoder, thus capturing both local details and global context information. Moreover, an Enhanced Feature Refinement Head is designed to finely re‐weight features on the channel dimension and narrow the semantic gap between shallow and deep features, thereby enhancing multi‐scale feature extraction. Additionally, an Edge‐Guided Context Module is introduced to enhance edge areas through effective edge detection, which can improve edge information extraction. Experimental results show that DEUFormer achieves an average Mean Intersection over Union (mIoU) of 53.8% on the LoveDA dataset and 69.1% on the UAVid dataset. Notably, the mIoU of buildings in the LoveDA dataset is 5.0% higher than that of UNetFormer. The proposed model outperforms methods such as UNetFormer on multiple datasets, which demonstrates its effectiveness.

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