基于自适应语义连接和感知注意力的沙漠分割方法

doi:10.11871/jfdc.issn.2096-742X.2026.02.003

数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (2): 25-39.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.02.003

doi: 10.11871/jfdc.issn.2096-742X.2026.02.003

• 专刊：冰冻圈大数据挖掘分析关键技术及应用 • 上一篇下一篇

基于自适应语义连接和感知注意力的沙漠分割方法

王兆滨^1,^*(),王睿¹,吕永科¹,张耀南²

¹ 兰州大学，信息科学与工程学院，甘肃兰州 730000
² 中国科学院西北生态环境资源研究院，甘肃兰州 730000

收稿日期:2025-07-23 出版日期:2026-04-20 发布日期:2026-04-23
通讯作者: *王兆滨（E-mail: wangzhb@lzu.edu.cn）
作者简介:王兆滨，兰州大学信息科学与工程学院，博士，教授，主要研究方向为智能信息处理与分析。
本文负责指导论文撰写、算法设计与实现。
WANG Zhaobin, Ph.D., is a professor and PhD supervisor at the School of Information Science and Engineering, Lanzhou University. His research interests include intelligent information processing and analysis.
In this paper, he is mainly responsible for providing guidance on manuscript writing, algorithm design and implementation.
E-mail: wangzhb@lzu.edu.cn
基金资助:
国家重点研发计划基础科研条件与重大科学仪器设备研发专项“冰冻圈大数据挖掘分析关键技术及应用”(2022YFF07117)

Desert Segmentation Based on Adaptive Semantic Connectivity and Perceptual Attention

WANG Zhaobin^1,^*(),WANG Rui¹,LYU Yongke¹,ZHANG Yaonan²

¹ School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, China
² Northwest Institute of Eco-Environment and Resources, CAS, Lanzhou, Gansu 730000, China

Received:2025-07-23 Online:2026-04-20 Published:2026-04-23

摘要/Abstract

摘要：

【背景】 荒漠化是威胁经济发展与生态安全的严重土地退化过程，卫星遥感影像具有覆盖范围广、分辨率高的特点，因此利用深度神经网络与遥感影像技术提取沙漠边界对科学研究和可持续发展具有重要意义。【方法】 受此启发，本文提出一种基于自适应语义连接的混合网络模型，通过融合全局上下文与局部纹理特征，有效降低编码器与解码器间的语义差异，提升沙漠边界的语义分割精度。为解决模型计算复杂度高、泛化能力不足的问题，设计动态特征缩放多头自注意力机制，结合残差卷积块注意力模块，增强模型对多尺度特征的动态捕获能力。此外，引入可微分边界度量作为损失函数，优化边界分割的连续性。【结果】 基于Landsat 8遥感影像数据集的实验表明，该模型在目视解译与定量评价指标中均表现出优越性能，为沙漠化监测提供了高精度的技术手段。

关键词: 深度学习, 语义分割, 遥感, 自适应语义连接

Abstract:

[Background] Desertification is recognized as a severe land degradation process threatening economic development and ecological security. Satellite remote sensing imagery is utilized for its wide coverage and high resolution, making deep neural networks and remote sensing techniques critical for desert boundary extraction in scientific research and sustainable development. [Methods] Inspired by this, a hybrid network model based on adaptive semantic connectivity is proposed, where global context and local texture features are fused to effectively reduce semantic discrepancies between encoder and decoder, thereby enhancing desert boundary segmentation accuracy. To address high computational complexity and insufficient generalization capability, a dynamic feature-scaled multi-head self-attention mechanism is designed, which is combined with a residual convolutional block attention module to strengthen multi-scale feature capture. Additionally, a differentiable boundary metric is introduced as a loss function to optimize boundary continuity. [Results] Experiments conducted on the Landsat-8 dataset demonstrate superior performance in both visual interpretation and quantitative evaluation metrics, and a high-precision technical solution is provided for desertification monitoring.

Key words: deep learning, semantic segmentation, remote sensing, adaptive semantic connectivity

王兆滨, 王睿, 吕永科, 张耀南. 基于自适应语义连接和感知注意力的沙漠分割方法[J]. 数据与计算发展前沿, 2026, 8(2): 25-39.

WANG Zhaobin, WANG Rui, LYU Yongke, ZHANG Yaonan. Desert Segmentation Based on Adaptive Semantic Connectivity and Perceptual Attention[J]. Frontiers of Data and Computing, 2026, 8(2): 25-39, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.02.003.

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参考文献 19

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ppa	DFS-MSA	ASC	OA (%)	MIoU (%)	MPA (%)	MRecall (%)	MF1 (%)
√			97.63	95.35	97.64	97.60	97.62
√	√		98.37	96.80	98.41	98.34	98.37
√	√	√	98.65	97.32	98.63	98.66	98.64

Loss1	Loss2	Loss3	OA (%)	MIoU (%)	MPA (%)	MRecall (%)	MF1 (%)
Ce-loss			97.91	95.90	97.90	97.91	97.91
	Dice-loss		98.00	96.07	97.97	98.03	97.99
Ce-loss	Dice-loss		98.52	97.08	98.51	98.52	98.52
Ce-loss	Dice-loss	Bou-loss	98.65	97.32	98.63	98.66	98.64

Methods	OA (%)	MIoU (%)	MPA (%)	MRecall (%)	MF1 (%)
UNet^[3]	92.11	85.33	92.10	92.06	92.08
PSPNet^[14]	93.95	88.53	94.07	93.82	93.91
S-UNet^[15]	95.12	90.67	95.14	95.08	95.11
DeepLab V3+^[9]	95.17	90.76	95.19	95.12	95.15
DAE-Former^[16]	95.55	91.46	95.55	95.53	95.54
Swin-UNet^[17]	96.22	92.70	96.23	96.20	96.21
HiFormer^[18]	96.44	93.11	96.45	96.41	96.43
MT-UNet^[19]	97.36	94.83	97.37	97.32	97.35
ASC-Trans	98.65	97.32	98.63	98.66	98.64

Methods	Params (M)	FLOPs (G)
UNet^[3]	24.45	31.20
PSPNet^[14]	46.73	68.12
S-UNet^[15]	63.25	52.36
DeepLab V3+^[9]	39.67	62.35
DAE-Former^[16]	48.07	26.16
Swin-UNet^[17]	41.38	34.79
HiFormer^[18]	73.85	23.21
MT-UNet^[19]	79.95	44.78
ASC-Trans	116.24	178.45

Methods	OA (%)	MIoU (%)	MPA (%)	MRecall (%)	MF1 (%)
PSPNet^[14]	95.50	87.27	92.66	93.43	93.04
UNet^[3]	96.24	89.45	92.97	95.88	94.33
S-UNet^[15]	96.65	90.28	94.58	95.04	94.80
DeepLab V3+^[9]	97.25	91.58	97.77	93.63	93.04
DAE-Former^[16]	96.70	90.29	95.30	94.33	94.80
Swin-UNet^[17]	97.21	91.78	95.64	95.67	95.65
HiFormer^[18]	97.58	92.92	95.65	96.96	96.29
MT-UNet^[19]	98.53	95.54	97.74	97.66	97.70
ASC-Trans	98.97	96.86	98.48	98.32	98.40

基于自适应语义连接和感知注意力的沙漠分割方法

Desert Segmentation Based on Adaptive Semantic Connectivity and Perceptual Attention

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参考文献 19

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Methods	OA (%)	MIoU (%)	MPA (%)	MRecall (%)	MF1 (%)
UNet^[3]	95.98	91.29	94.78	96.16	95.42
PSPNet^[14]	96.57	92.39	96.15	95.89	96.02
DeepLab V3+^[9]	96.58	92.38	96.27	95.78	96.02
S-UNet^[15]	96.74	93.56	96.86	96.51	96.67
DAE-Former^[16]	96.96	93.20	96.74	96.21	96.46
Swin-UNet^[17]	97.53	94.44	97.24	97.03	97.13
MT-UNet^[19]	97.76	95.54	97.75	97.70	97.72
HiFormer^[18]	98.08	95.65	98.07	97.49	97.77
ASC-Trans	98.73	97.45	98.68	98.74	98.71

Methods	OA (%)	MIoU (%)	MPA (%)	MRecall (%)	MF1 (%)
PSPNet^[14]	95.34	90.91	95.53	95.01	95.23
UNet^[3]	95.99	92.15	95.93	95.90	95.91
S-UNet^[15]	96.56	92.40	95.97	96.10	96.03
DeepLab V3+^[9]	97.12	94.30	97.09	97.04	97.07
DAE-Former^[16]	97.22	94.50	97.26	97.08	97.17
Swin-UNet^[17]	97.44	94.92	97.47	97.32	97.39
MT-UNet^[19]	97.94	95.33	98.07	97.16	97.60
HiFormer^[18]	97.99	95.99	98.02	97.89	97.96
ASC-Trans	98.73	97.45	98.71	98.81	98.76

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