空间信息引导扩散的遥感图像领域自适应语义分割

梁燕; 李俊范; 邵凯; 胡林

doi:10.11999/JEIT260031

空间信息引导扩散的遥感图像领域自适应语义分割

doi: 10.11999/JEIT260031 cstr: 32379.14.JEIT260031

梁燕^{1, 2, ,},
李俊范^{1, 2,},
邵凯¹,
胡林¹

1.
重庆邮电大学，通信与信息工程学院，重庆 400065
2.
信号与信息处理重庆市重点实验室，重庆 400065

基金项目: 重庆市自然科学基金 (CSTB2025NSCQ-GPX1253)

详细信息

作者简介:
梁燕：女，高级工程师，主要研究领域为嵌入式人工智能、遥感图像处理

李俊范：男，硕士研究生，主要研究领域为扩散模型、遥感图像处理、领域自适应、语义分割

邵凯：男，副教授，主要研究领域为智能感知与信息系统、信号与信息智能处理

胡林：男，讲师，主要研究领域为嵌入式人工智能、遥感图像处理、信号与信息智能处理

通讯作者:
梁燕　liangyan@cqupt.edu.cn

中图分类号: TP192.14
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- 文章访问数: 36
- HTML全文浏览量: 10
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- 被引次数: 0
出版历程
- 收稿日期: 2026-01-09
- 修回日期: 2026-03-15
- 录用日期: 2026-04-09
- 网络出版日期: 2026-04-27

Spatial Information-Guided Diffusion for Remote Sensing Image Domain Adaptation Semantic Segmentation

LIANG Yan^{1, 2
, ,},
LI Jun-Fan^{1, 2
,},
SHAO Kai¹,
HU Lin¹

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065
2.
Chongqing Key Laboratory of Signal and Information Processing, Chongqing 400065

Funds: Natural Science Foundation of Chongqing (CSTB2025NSCQ-GPX1253)

摘要

摘要: 为提高遥感图像域自适应语义分割Domain Adaptation Semantic Segmentation(DASS)的跨域适应效果，本文提出基于协同训练与空间引导扩散模型的域自适应语义分割框架Co-Training Spatial-Guided DASS(CoSG-DASS)。CoSG-DASS使用空间引导扩散模型构成图像翻译减少域间差异，并利用协同训练提升模型对目标域的自适应能力：在图像翻译阶段，设计新型空间信息引导扩散模型，通过向潜在扩散模型注入水平语义分布伪标签与垂直语义分布深度估计重构空间引导信息，实现源域到目标域的语义无偏转换。其中针对伪标签质量问题，提出基于熵的引导强度自适应模块，通过熵值筛选高置信度区域特征以抑制噪声干扰，有效提升跨域成像差异下的语义对齐精度；在协同训练阶段，提出融合深度信息与对抗损失的训练策略，通过增强多维知识表征缩减类内差异并增大类间差异，提升模型的跨域自适应能力。通过在三类典型遥感跨域差异任务(跨地理环境、跨成像模式、标签语义异质)中仿真验证，本文所提CoSG-DASS表现优异，相较于已有方法在平均交并比(mIoU)分别有1.14%、3.78%和2.49%的提升。
- 扩散模型 /
- 领域自适应 /
- 语义分割 /
- 遥感图像 /
- 信息熵
Abstract: Objective Domain Adaptive Semantic Segmentation (DASS) is critical for remote sensing applications including land cover mapping, urban planning and environmental monitoring. However, deep learning models suffer severe performance degradation due to domain shifts caused by imaging variations, geographic differences and label semantic heterogeneity. Traditional alignment and GAN-based methods often fail to preserve semantic consistency and are sensitive to noisy supervision, especially under large cross-domain gaps. This work aims to build a robust DASS framework for semantically consistent image translation and effective knowledge transfer. Methods We propose Co-training Spatial-Guided DASS (CoSG-DASS), a two-stage framework integrating image translation and collaborative training. A spatial-guided latent diffusion model enhanced with ControlNet is designed, using pseudo-labels and depth estimates as spatial conditions. To mitigate noisy pseudo-labels, we introduce an Entropy-based Adaptive Guidance Intensity Module (EAGIM) that evaluates pixel confidence via information entropy and suppresses unreliable features. In collaborative training, translated target-style images and unlabeled real target images jointly train a segmentation model with a depth-aware decoder, using a hybrid loss of cross-entropy and adversarial loss. Results and Discussions Extensive experiments verify CoSG-DASS on three cross-domain tasks. It generates images closer to target-domain distributions. Quantitative results (FID, IS) demonstrate its superiority over CycleGAN, UNI-Diff, and CRS-Diff (Table 1). Visual comparisons (Fig.6) demonstrate that our method reduces edge blur and category confusion, such as better distinguishing roads from vegetation and preserving small objects like vehicles. In the semantic segmentation stage, CoSG-DASS significantly outperforms state-of-the-art domain adaptation methods. It achieves mIoU improvements of 1.14%, 3.78%, and 2.49% on the cross-geography (Vaihingen IRRG→Potsdam IRRG), cross-modality (Vaihingen IRRG→Potsdam RGB),and label heterogeneity (DFC25↔LoveDA) tasks, respectively (Table 2, Table 3, Table 4). Segmentation result visualizations (Fig.7) confirm its strong boundary preservation and accuracy in complex scenes. Ablation studies (Table 5) verify the contribution of each core component (depth control, pseudo-label guidance, EAGIM, and co-training strategy). Feature distribution visualization using UMAP (Fig.8) further illustrates that CoSG-DASS effectively compacts intra-class features and separates inter-class features after adaptation. Conclusions CoSG-DASS effectively alleviates domain shifts in remote sensing images via semantic-preserving diffusion translation and depth-aware co-training. It outperforms existing methods on both translation quality and segmentation accuracy, providing a valuable solution for multi-source remote sensing interpretation. Future work will target extreme label heterogeneity and lightweight diffusion designs.
- Diffusion Models /
- Domain Adaptation /
- Semantic Segmentation /
- Remote Sensing Images /
- Information Entropy

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图 1 不同数据集之间的域差异

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图 2 CoSG-DASS总体框架图

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图 3 残差特征提取模块RFEM

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图 4 基于信息熵的引导强度自适应模块结构EAGIM

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图 5 自适应分割训练策略

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图 6 图像翻译质量结果可视化

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图 7 对比实验模型语义分割结果可视化

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图 8 UMAP 语义分割输出特征可视化

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图 9 协同训练策略在训练稳定性中的贡献

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表 1 图像翻译质量结果量化对比

生成模型	场景(1)	场景(2)	场景(3)
生成模型	FID	FID	FID	FID
CycleGAN[11]	74.25	74.6	111.12	96.87
DiscoGAN[20]	102.82	72.77	103.71	95.55
UNI-Diff[14]	64.72	59.83	90.16	80.57
CRS-Diff[15]	74.49	74.46	94.71	79.66
COSG-DASS	72.61	69.73	89.91	82.76

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表 2 跨地理区域场景Vaihingen IRRG(源域)至Potsdam IRRG(目标域)语义分割对比(*%)

任务	自适应方法	不透水层	建筑物	低矮植被	树木	汽车	其它	mIoU
Vaihingen IRRG ↓ Potsdam IRRG	未适应	57.82	44.82	22.18	28.61	69.60	23.31	44.61
	仅源域	86.49	90.71	73.70	73.62	74.20	72.01	79.74
	CycleGAN[11]	58.01	67.46	38.61	39.19	57.02	22.06	52.06
	DiscoGAN[20]	59.08	66.61	44.94	34.78	54.23	16.35	51.93
	CyCADA[12]	12.55	29.24	47.38	51.27	3.53	9.83	28.79
	CLAN[21]	66.29	72.66	42.72	45.48	67.46	21.44	58.92
	FADA[22]	65.73	73.96	43.36	46.72	59.03	19.03	57.76
	CorDA[10]	67.39	69.43	54.83	48.67	68.52	9.30	61.77
	AdaptSegNet[23]	66.33	70.80	52.03	44.61	70.10	21.80	60.77
	DAFormer[24]	68.45	76.18	53.67	49.52	68.93	20.12	62.85
	DIFF[25]	67.82	73.54	52.51	49.03	68.21	17.84	62.10
	UNI-Diff[14]	69.81	78.73	51.95	50.28	67.84	23.57	63.72
	CRS-Diff[15]	67.81	80.45	58.40	50.74	69.44	18.56	65.37
	OUR(COSG-DASS)	70.27	79.78	54.99	55.70	71.81	21.71	66.51

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表 3 跨成像模式场景Vaihingen IRRG(源域)至Potsdam RGB(目标域)语义分割对比(*%)

任务	自适应方法	不透水层	建筑物	低矮植被	树木	汽车	其它	mIoU
Vaihingen IRRG ↓ Potsdam RGB	未适应	50.36	43.60	1.81	2.35	69.64	11.89	33.55
	仅源域	86.49	90.71	73.70	73.62	74.20	72.01	79.74
	CycleGAN[11]	53.80	64.03	32.35	33.05	50.78	17.68	46.80
	DiscoGAN[20]	65.08	69.93	49.13	41.16	59.49	18.89	56.92
	CyCADA[12]	35.29	42.24	26.51	24.70	6.86	7.82	27.12
	CLAN[21]	62.98	71.91	47.51	47.77	66.19	10.91	59.27
	FADA[22]	63.99	73.56	37.11	39.57	59.01	15.27	54.65
	CorDA[10]	65.93	72.52	51.80	46.47	64.63	15.78	60.27
	AdaptSegNet[23]	58.65	65.31	37.95	39.34	69.70	14.34	54.19
	DAFormer[24]	63.25	71.38	48.76	46.82	65.74	14.85	58.80
	DIFF[25]	64.87	74.06	50.24	47.15	66.82	15.93	59.85
	UNI-Diff[14]	60.27	72.63	42.51	45.64	68.67	11.47	57.94
	CRS-Diff[15]	59.63	76.01	44.37	43.38	69.41	13.22	58.56
	OUR(COSG-DASS)	66.80	79.80	50.20	44.78	70.15	6.56	62.34

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表 4 语义异质性场景(DFC25与LoveDA数据集互相迁移)语义分割对比(*%)

任务	自适应方法	背景	建筑	道路	水体	裸地/开发区区	森林/树木木	耕地	mIoU
DFC25 ↓ LoveDA	未适应	24.83	53.47	41.57	64.90	1.13	32.88	18.30	36.46
	仅源域	76.15	81.78	68.86	88.50	71.74	83.60	72.91	78.44
	CycleGAN[11]	23.19	53.31	43.69	65.50	3.79	32.53	21.85	37.00
	DiscoGAN[20]	23.45	50.34	42.72	56.92	1.35	28.57	12.43	33.89↓
	CyCADA[12]	26.30	46.72	41.11	52.93	0.78	32.78	14.04	33.43↓
	CLAN[21]	26.99	52.08	39.79	68.76	0.77	31.85	15.61	36.71
	FADA[22]	24.85	52.41	42.07	61.32	1.14	33.04	18.22	35.81↓
	CorDA[10]	37.50	38.77	23.25	63.56	3.32	64.07	7.69	38.41
	AdaptSegNet[23]	25.69	52.80	46.38	57.42	1.63	36.33	19.47	36.71
	DAFormer[24]	24.97	53.11	48.25	70.12	2.18	34.86	20.35	38.25
	DIFF[25]	23.86	52.94	47.53	69.87	1.95	34.22	19.87	37.75
	UNI-Diff[14]	21.44	53.07	45.70	70.69	2.36	34.75	20.68	38.00
	CRS-Diff[15]	20.86	52.29	46.61	73.46	2.54	35.72	18.23	38.58
	OUR(COSG-DASS)	25.82	53.24	49.67	71.64	1.27	33.94	20.46	39.26
LoveDA ↓ DFC25	未适应	22.28	48.03	37.42	45.85	9.13	47.57	3.43	35.05
	仅源域	40.42	63.82	61.46	71.09	33.08	43.99	43.17	52.31
	CycleGAN[11]	22.07	53.48	29.43	58.74	4.95	29.17	8.69	32.97↓
	DiscoGAN[20]	24.10	48.54	30.85	55.80	6.20	52.43	5.16	36.32
	CyCADA[12]	21.60	50.31	32.12	39.50	4.05	49.37	6.99	32.82↓
	CLAN[21]	22.24	49.10	33.25	41.58	6.26	46.73	11.66	33.19↓
	FADA[22]	28.23	48.40	32.32	53.69	4.61	45.78	10.64	35.50
	CorDA[10]	24.07	39.07	31.09	49.78	7.74	22.77	26.23	29.09↓
	AdaptSegNet[23]	21.59	47.48	29.72	51.83	11.05	54.99	6.21	36.11
	DAFormer[24]	30.08	49.82	33.56	65.43	5.17	59.04	11.3	39.2
	DIFF[25]	29.54	48.76	32.18	64.05	3.82	58.47	10.68	38.5
	UNI-Diff[14]	29.22	48.27	31.03	63.32	2.63	58.32	3.22	38.80
	CRS-Diff[15]	30.63	50.65	31.01	60.90	5.04	59.62	10.57	39.64
	OUR(COSG-DASS)	31.55	50.19	35.47	70.07	6.20	59.32	14.40	42.13

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表 5 全局模块消融实验

任务	基线	深度控制	伪标签	EAGIM	协同训练	mIoU (%)	增量(%)
Vaihingen IRRG ↓ Potsdam RGB	√					33.55	----
	√	√				55.64	22.09
	√		√			50.16	16.61
	√	√	√	√		58.23	24.68
	√				√	35.76	0.21
	√	√	√	√	√	62.34	28.8
DFC25 ↓ LoveDA	√					36.46	----
	√	√				37.73	1.27
	√		√			37.19	0.73
	√	√	√	√		38.76	2.30
	√				√	37.36	0.90
	√	√	√	√	√	39.26	2.80

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表 6 EAGIM系数对模型精度的影响(mIoU*%)

EAGIM系数	跨地理区域	跨成像模式	标签语义异质
0.3	64.86	60.12	37.06
0.5	66.51	62.08	42.13
0.7	65.72	60.65	41.95
0.9	65.13	59.43	39.63

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表 7 $ {\lambda }_{\text{adv}} $参数对模型精度影响实验

$ {\lambda }_{\text{adv}} $	0.05	0.10	0.15	0.20	0.50
V IR→P R	62.20	62.34	62.33	61.31	61.74
V IR→P IR	66.34	66.51	65.56	64.17	63.12
D→L	39.65	39.26	39.11	39.07	39.12
L→D	41.15	42.13	42.14	40.42	39.07

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