MAFUNet：结合注意力机制和主动轮廓损失的SAR图像水体分割算法

doi:10.11947/j.AGCS.2025.20240235

测绘学报 ›› 2025, Vol. 54 ›› Issue (5): 924-936.doi: 10.11947/j.AGCS.2025.20240235

MAFUNet：结合注意力机制和主动轮廓损失的SAR图像水体分割算法

邢广澳(), 卢官明, 韩斌()

南京邮电大学通信与信息工程学院，江苏　南京　210003

收稿日期:2024-06-04 修回日期:2025-04-10 出版日期:2025-06-23 发布日期:2025-06-23
通讯作者: 韩斌 E-mail:guangaoxing1215@163.com;njupt.bh@foxmail.com
作者简介:邢广澳（1999—），男，硕士生，研究方向为遥感图像处理。　E-mail：guangaoxing1215@163.com
基金资助:
国家自然科学基金(62201281);江苏省自然科学基金(BK20220392);南京邮电大学引进人才自然科学研究启动基金(NY222004)

MAFUNet: water body segmentation algorithm for SAR images combining attention mechanisms and active contour loss

Guangao XING(), Guanming LU, Bin HAN()

School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2024-06-04 Revised:2025-04-10 Online:2025-06-23 Published:2025-06-23
Contact: Bin HAN E-mail:guangaoxing1215@163.com;njupt.bh@foxmail.com
About author:XING Guangao (1999—), male, postgraduate, majors in remote sensing image processing.　E-mail: guangaoxing1215@163.com
Supported by:
The National Natural Science Foundation of China(62201281);The Natural Science Foundation of Jiangsu Province(BK20220392);The Natural Science Research Initiation Fund for Talents Introduced to Nanjing University of Posts and Telecommunications(NY222004)

摘要/Abstract

摘要：

随着遥感技术的日益发展，合成孔径雷达（SAR）图像已成为探测地表水体的主要方法之一。由于存在复杂的干扰，SAR图像中的水体分割仍然是一项具有挑战性的任务。为了实现精确的水体分割，受UNet在分割边缘较弱的小目标时的有效性启发，提出了一种多层注意力融合UNet（MAFUNet）。首先，在编码器和解码器部分之间的跳接连接中加入了空间注意力模块（SAM）和通道注意力模块（CAM），以提取有用的低级和高级特征，弥补降采样带来的语义信息损失。其次考虑到上采样会导致图像特征失真，设计了一种注意力上采样器（AU），保留了图像更多的细节信息，降低了噪声的影响。在解码器最后一层引入多尺度卷积池块（MCPB），以更好地利用上下文信息，捕捉不同尺度的水和陆地特征。此外，还设计了主动轮廓损失作为额外的正则化，采用多层损失函数优化网络，更好地提取层级特征，从而提高模型的分割性能。试验结果表明，MAFUNet优于其他先进的模型，在ALOS PALSAR数据集上的IoU和F₁值分别为94.28%和97.67%。

关键词: 水体分割, SAR, 注意力融合UNet, MCPB, 主动轮廓损失

Abstract:

With the increasing development of remote sensing technology, synthetic aperture radar (SAR) imagery has become one of the main methods for water body segmentation. Due to the presence of complex interference, water body segmentation in SAR imagery is still a challenging task. To achieve accurate water body detection, we proposed a multi-level attention fusion UNet (MAFUNet) inspired by the effectiveness of UNet in segmenting small targets with weak edges. First, the spatial attention module (SAM) and channel attention module (CAM) are added to the skip connections between the encoder and decoder parts to extract useful low-level and high-level features, compensating for the loss of semantic information of downsampling. Second, considering the feature distortion resulting from upsampling, an attention upsampler (AU) is designed that retains more detailed information of the image and reduces the effect of introduced noise. Third, the multiscale convolutional pooling block (MCPB) is introduced into the decoder part to better utilize the contextual information, capturing water and land features at different scales. Moreover, an active contour loss is designed as an additional regularization, and a multilayer loss function is used to optimize the network for better extraction of layer-level features, improving the model's segmentation performance. The experimental results show that the proposed MAFUNet outperforms other state-of-the-art models, IoU and F₁ are 94.28% and 97.67% on the ALOS PALSAR dataset.

Key words: water body segmentation, SAR, attention fusion UNet, MCPB, active contour loss

中图分类号:

P234

邢广澳, 卢官明, 韩斌. MAFUNet：结合注意力机制和主动轮廓损失的SAR图像水体分割算法[J]. 测绘学报, 2025, 54(5): 924-936.

Guangao XING, Guanming LU, Bin HAN. MAFUNet: water body segmentation algorithm for SAR images combining attention mechanisms and active contour loss[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(5): 924-936.

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层	输入	输出	结构
Conv 0	3×256×256	64×256×256	（3×3Conv+BN+ReLU）×2
Layer 1	64×256×256	64×128×128	7×7Conv+BN+ReLU
Layer 2	64×128×128	64×64×64
Layer 3	64×64×64	128×32×32
Layer 4	128×32×32	256×16×16
Layer 5	256×16×16	512×8×8

数据集划分	数据集	样本数量	图像尺寸
训练集		1413	256×256
验证集	ALOS PALSAR	706	256×256
测试集		235	256×256

模型	OA	Pre	R	IoU	F₁值
UNet	88.51	87.53	88.52	89.22	88.02
AUNet	88.01	89.09	90.92	91.15	90.01
BSGNet	88.89	88.45	89.56	90.32	89.00
SANet	93.34	92.02	92.35	92.35	92.18
MP-ResNet	93.56	92.78	92.68	92.56	92.45
DAUNet	94.04	93.02	93.13	92.41	93.08
MAFUNet	97.23	97.69	97.66	94.28	97.67

模型	OA	Pre	R	IoU	F₁值
DAUNet	94.04	93.02	93.13	92.41	93.08
AFCNet	93.68	91.78	91.26	91.47	91.51
DPPAUNet	94.23	93.85	92.62	92.22	93.23
DDUNet	93.62	91.21	91.63	90.42	91.41
SAUNet	96.23	94.52	93.26	92.63	93.88
MRSE-Net	96.63	94.25	94.33	93.62	94.29
MAFUNet	97.23	97.69	97.66	94.28	97.67

（α₁，β₁）	IoU	F₁值
（0，1）	91.68	94.23
（0.25，0.75）	92.88	96.43
（0.5，0.5）	92.72	95.73
（0.75，0.25）	92.65	95.56
（1，0）	90.68	93.02

MAFUNet：结合注意力机制和主动轮廓损失的SAR图像水体分割算法

MAFUNet: water body segmentation algorithm for SAR images combining attention mechanisms and active contour loss

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图/表 15

参考文献 30

相关文章 15

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Metrics

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网络	IoU	F₁值
MAFUNet+SENet	92.88	96.35
MAFUNet+SKNet	93.15	96.52
MAFUNet+CBAM（本文方法）	94.28	97.67

网络	OA	Pre	R	IoU	F₁值
Baseline	93.36	94.87	93.92	92.02	94.39
Baseline+SA+CA	93.55	95.78	95.13	92.53	95.45
Baseline+AU	93.75	95.82	95.65	92.64	95.73
Baseline+MCPB	94.88	96.34	95.28	92.48	95.80
Baseline+SA+CA+AU	95.08	96.45	95.78	92.82	96.11
MAFUNet	97.23	97.69	97.66	94.28	97.67

网络	IoU	F₁值
Baseline+BCE	90.32	92.86
Baseline+BCE+Dice	91.64	93.32
Baseline+BCE+AC	92.02	94.39
MAFUNet+BCE	92.63	96.76
MAFUNet+BCE+Dice	93.35	6.78
MAFUNet+BCE+AC	94.28	97.67
（本文方法）	94.28	97.67

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