Heterogeneous remote sensing change detection based on vision-language collaborative representation for flood disasters

doi:10.11947/j.AGCS.2026.20250521

Abstract

Abstract:

Heterogeneous change detection using optical and SAR imagery is of great significance for disaster emergency response and all-weather monitoring. However, the significant differences in their imaging mechanisms lead to inconsistent feature distributions, which, coupled with the lack of annotated samples and textual descriptions, restrict the detection performance of traditional methods and existing deep learning models. To this end, this paper proposes a multi-dimensional change enhancement CLIP change detection network (MCE-CLIP), aiming to tackle the challenges of heterogeneous image change detection in flood disaster scenarios. The network constructs a cross-modal semantic guidance mechanism based on “SAR image transfer-text generation”, effectively narrowing the semantic gap between heterogeneous images. Meanwhile, a pseudo-siamese visual feature extraction branch and a multi-dimensional change feature enhancement module (MCFEM) are designed. By embedding modality adapters, the domain distribution discrepancy of remote sensing images is reduced. Furthermore, the MCFEM is constructed by integrating temporal cross-attention, multi-granularity differencing, and hybrid similarity projection, achieving the efficient integration of spatiotemporal contextual information. Experimental results on two typical heterogeneous datasets demonstrate that MCE-CLIP outperforms existing mainstream heterogeneous change detection methods in core evaluation metrics such as F₁ score and intersection over union.

Key words: change detection, heterogeneous remote sensing imagery, vision-language model, multi-modal fusion, SAR

CLC Number:

P237

Rui YU, Jie LI, Huihui LIU, Meiru WU, Liupeng LIN, Qiangqiang YUAN, Li ZHENG. Heterogeneous remote sensing change detection based on vision-language collaborative representation for flood disasters[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(5): 927-940.

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类别分类	基础地物实体	洪水灾害状态描述
对象级	河流、湖泊、池塘、湿地、海洋	河流溢出、水体扩张、浑浊水域、深水区
	森林、农田、草地、裸土、植被、沙漠、山脉、丛林、岛屿	被淹没的农田、植被受损、泥泞土地、浸水区
	道路、桥梁、建筑物、住宅区、商业区、工业区、校园、广场、停车场、不透水地表	被淹没的道路、切断的交通、房屋被围困、城市内涝
场景级	乡村场景、城市景观、沿海地区、沿河地区	大范围淹没、洪水灾害区域、灾后废墟

类型	方法	OA	Pr	Re	F₁值	IoU
无监督	INLPG	88.84	42.90	40.81	41.83	26.44
	SCCN	80.22	29.67	71.98	42.02	26.60
	ACE-Net	91.02	54.04	57.68	55.80	38.70
风格迁移	FC-EF	97.18	90.90	79.93	85.06	74.00
	E-UNet	97.17	85.22	86.20	85.71	74.99
	DTCDN	98.98	94.98	94.89	94.93	90.34
	GLCD-DA	99.20	95.24	96.65	95.94	92.20
VLM	ChangeCLIP	98.65	95.75	90.30	92.94	86.82
	VLCD	98.43	97.84	85.93	91.50	84.33
	MCE-CLIP	99.22	96.23	95.87	96.05	92.84

类型	方法	OA	Pr	Re	F₁值	IoU
无监督	INLPG	91.99	45.66	58.81	51.41	34.60
	SCCN	88.02	39.15	89.85	54.54	37.49
	ACE-Net	92.26	47.31	65.36	54.89	37.82
风格迁移	FC-EF	94.55	70.60	60.42	65.12	48.28
	E-UNet	95.69	70.84	68.55	69.67	53.46
	DTCDN	95.19	64.15	75.09	69.19	52.90
	GLCD-DA	95.76	68.68	75.62	71.98	56.23
VLM	ChangeCLIP	95.65	70.74	67.55	69.11	52.80
	VLCD	95.86	76.06	62.08	68.36	51.93
	MCE-CLIP	96.02	72.56	71.97	72.27	56.58

数据集	指标	GLCD-DA	MCE-CLIP	p值	显著性
Gloucester Ⅰ	F₁值	95.32±0.38	95.98±0.17	p<0.01	显著
Gloucester Ⅰ	IoU	91.05±0.45	92.27±0.21	p<0.01	显著
California	F₁值	71.35±0.42	72.18±0.11	p<0.01	显著
California	IoU	55.45±0.51	56.40±0.15	p<0.01	显著

输入影像类型	核心模块			Gloucester Ⅰ数据集					California数据集
输入影像类型	文本提示	模态适配器	MCFEM	OA	Pr	Re	F₁值	IoU	OA	Pr	Re	F₁值	IoU
光学/SAR	×	×	×	98.69	98.48	88.06	92.98	86.88	94.86	61.27	77.91	68.59	52.20
光学/SAR	√	×	×	98.89	98.00	90.57	94.14	88.93	94.81	60.62	79.86	68.92	52.58
光学/SAR	√	√	×	98.92	97.11	91.76	94.36	89.32	95.90	76.77	61.73	68.44	52.02
光学/SAR	√	×	√	99.14	96.45	94.75	95.59	91.56	95.88	75.35	63.57	68.96	52.63
光学/伪光学	√	√	√	99.18	97.29	94.05	95.79	91.92	95.61	66.76	77.89	71.90	56.13
光学/SAR	√	√	√	99.22	96.23	95.87	96.05	92.84	96.02	72.56	71.97	72.27	56.58