Author(s)

Yaxin Dong ¹, Yang Yang ¹

Affiliation(s)

¹ The Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming, China

Corresponding Author

Yang Yang

ABSTRACT

Change detection plays a crucial role in identifying differences between multi-temporal images captured over the same geographical area, with applications spanning various fields including urban planning, environmental monitoring, and disaster assessment. However, challenges persist in handling bitemporal images with viewpoint difference, affecting the performance of traditional change detection models. To address these challenges, this paper proposes a novel end-to-end optical flow alignment change detection (PFCD) model. The PFCD model integrates optical flow estimation technology, enabling direct change detection in images with viewpoint differences without the need for a separate image registration model. Through end-to-end training, the model achieves higher detection accuracy and faster processing speeds. Experimental results on the scattered garbage regions change detection dataset (SGRCD-VD) and the building change detection dataset (WHUCD-VD) validate the effectiveness of the model. On the SGRCD-VD test set, the PFCD model achieves an F1 score of 91.00%, while on the WHUCD-VD test set, it reaches 94.82%, demonstrating excellent performance in handling images with viewpoint differences. Additionally, the model exhibits advantages in processing speed and model parameter.

KEYWORDS

Deep learning, change detection, optical flow estimation, viewpoint difference

CITE THIS PAPER

Yaxin Dong, Yang Yang, Change Detection in Images with Viewpoint Difference. Advances in Computer, Signals and Systems (2024) Vol. 8: 69-74. DOI: http://dx.doi.org/10.23977/acss.2024.080211.

REFERENCES

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