PID-CD: PID Control is What Change Detection Needs

Remote Sensing Change Detection (RSCD) is a remote sensing technique used to identify geomorphological changes in bi-temporal images of the same region. However, the identification of change regions is often hindered by false changes in similar objects, resulting in inaccurate boundary recognition of the change regions. This paper adopts the Proportional-Integral-Derivative (PID) control concept and designs two modules: PID Feature Alignment(PIDFA) and PID Feature Fusion (PIDFF), which makes up the PID-CD model. The PIDFA enhances and corrects the change region features using manifold structures and adaptive strategies, while the PIDFF introduces a third branch to guide multi-level feature fusion, progressively refining the boundaries of the change regions. Finally, extensive experiments on benchmark datasets, including LEVIR-CD, S2Looking, and CL-CD, display that PID-CD achieve state-of-the-art performance in RSCD. Specifically, PID-CD achieves the F1 score of 91.94% on LEVIR-CD, 66.61% on S2Looking, and 74.11% on CL-CD, showing significant improvements in detail and boundary of change area.

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Publication Details

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/jstars.2025.3556096
Primary Topic
Advanced Control Systems Optimization
Type
article
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article

PID-CD: PID Control is What Change Detection Needs

Zhina Song, Liye Mei, Chuan Xu, Xudong Lai et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Advanced Control Systems Optimization
article

PID-CD: PID Control is What Change Detection Needs

Zhina Song, Liye Mei, Chuan Xu, Xudong Lai, Lingyu Yan, Qiyi He, Mingwei Wang, Zhiwei Ỹe, Zhening Fang, Shirui Sheng
article en

Abstract

Remote Sensing Change Detection (RSCD) is a remote sensing technique used to identify geomorphological changes in bi-temporal images of the same region. However, the identification of change regions is often hindered by false changes in similar objects, resulting in inaccurate boundary recognition of the change regions. This paper adopts the Proportional-Integral-Derivative (PID) control concept and designs two modules: PID Feature Alignment(PIDFA) and PID Feature Fusion (PIDFF), which makes up the PID-CD model. The PIDFA enhances and corrects the change region features using manifold structures and adaptive strategies, while the PIDFF introduces a third branch to guide multi-level feature fusion, progressively refining the boundaries of the change regions. Finally, extensive experiments on benchmark datasets, including LEVIR-CD, S2Looking, and CL-CD, display that PID-CD achieve state-of-the-art performance in RSCD. Specifically, PID-CD achieves the F1 score of 91.94% on LEVIR-CD, 66.61% on S2Looking, and 74.11% on CL-CD, showing significant improvements in detail and boundary of change area.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Wuhan University (CN), Hubei University of Technology (CN)
Openalex Percentile: Top 93%
Advanced Control Systems Optimization
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PID-CD: PID Control is What Change Detection Needs — Zhina Song, Liye Mei, et al. · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2025) | TGRS Research Map | TGRS