Remote-Sensing Image-Change Detection Based on Depth-Information Fusion

Jingyu Yang; Yahui An; Jianwu Dang; Feng Wang; Jiuyuan Huo

doi:10.3788/LOP242039

Laser & Optoelectronics Progress, Volume. 62, Issue 10, 1028004(2025)

Remote-Sensing Image-Change Detection Based on Depth-Information Fusion

Jingyu Yang^1,2、*, Yahui An¹, Jianwu Dang^1,2,3, Feng Wang⁴, and Jiuyuan Huo¹

Author Affiliations

¹School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu , China

²Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Lanzhou 730070, Gansu , China

³National Virtual Simulation Experimental Teaching Center for Rail Transit Information and Control, Lanzhou Jiaotong University, Lanzhou 730070, Gansu , China

⁴Gansu Luqiao Feiyu Transportation Facilities Co., Ltd., Lanzhou 730070, Gansu , China

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Figures & Tables(11)

Fig. 1. Overall Structure

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Fig. 2. Alignment mask module

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Fig. 3. Feature selection block

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Fig. 4. Feature distribution histogram

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Fig. 5. Visualization results of different models on the LEVIR-CD change detection dataset

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Fig. 6. Visualization results of different models on the CDD change detection dataset

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Fig. 7. Visualization results demonstrating effectiveness of selection fusion module

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Fig. 8. Visualization results demonstrating effectiveness of alignment mask module

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Fig. 9. Visualization results demonstrating effectiveness of depth information

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Table 1. Comparison results of different models on two datasets

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Table 1. Comparison results of different models on two datasets

Model	LEVIR-CD				CDD
Model	P	R	IoU	F₁	P	R	IoU	F₁
FC-EF^［33］	86.91	80.17	71.53	83.40	83.04	55.56	49.90	66.58
FC-Siam-Diff^［33］	89.53	83.31	75.92	86.31	92.27	54.34	51.98	68.40
FC-Siam-Conc^［33］	91.99	76.77	71.96	83.69	91.68	64.57	60.99	75.77
STANet-BAM^［30］	74.97	94.61	71.90	83.66	73.06	94.59	70.13	82.44
STANet-PAM^［30］	81.63	92.00	76.22	86.51	83.36	97.84	81.85	90.02
Unet++MSOF^［3］	88.52	85.29	76.80	86.87	89.54	87.11	79.06	88.30
IFN^［37］	94.02	82.93	78.77	88.13	90.56	70.18	65.40	79.08
SNUNet^［34］	89.18	87.17	78.83	88.16	90.30	86.41	72.11	83.89
SILICD^［36］	90.55	86.30	79.18	88.38	89.53	77.26	70.86	82.94
SSCD^［35］	91.04	87.35	80.44	89.15	92.26	90.15	83.81	91.20
DTCDSCN^［11］	90.05	88.35	80.49	89.19	84.75	88.95	74.36	86.80
FDCDN（ours）	92.15	88.62	82.40	90.35	93.57	91.65	86.22	92.60

Table 2. Quantitative evaluation of ablation study using the LEVIR-CD and CDD datasets

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Table 2. Quantitative evaluation of ablation study using the LEVIR-CD and CDD datasets

Model	LEVIR-CD				CDD
Model	P	R	IoU	F1	P	R	IoU	F1
M1	89.05	84.32	76.40	86.62	88.45	87.93	78.87	88.19
M2	88.15	86.17	77.22	87.15	89.33	88.54	80.07	88.93
M3	91.02	87.13	80.23	89.03	91.30	89.41	82.39	90.35
FDCDN	92.15	88.62	82.40	90.35	93.57	91.65	86.22	92.60

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Jingyu Yang, Yahui An, Jianwu Dang, Feng Wang, Jiuyuan Huo. Remote-Sensing Image-Change Detection Based on Depth-Information Fusion[J]. Laser & Optoelectronics Progress, 2025, 62(10): 1028004

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Paper Information

Category: Remote Sensing and Sensors

Received: Sep. 24, 2024

Accepted: Nov. 26, 2024

Published Online: May. 8, 2025

The Author Email: Jingyu Yang (yangjy@mail.lzjtu.cn)

DOI:10.3788/LOP242039

CSTR:32186.14.LOP242039

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology