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Remote Sensing
Volume 18
Issue 10
10.3390/rs18101673
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

POCA-Lite: A Lightweight Change-Detection Architecture with Geometry-Aware Auxiliary Supervision and Feedback Fusion

by
Yongqi Shi
1,*,
Ruopeng Yang
2,
Bo Huang
1,
Zhaoyang Gu
1,
Yiwei Lu
2,
Changsheng Yin
2,
Yongqi Wen
1 and
Yihao Zhong
1
1
Graduate School, National University of Defense Technology, Wuhan 430035, China
2
Department of Information and Communication Command, Information Support Force Engineering University, Wuhan 430035, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(10), 1673; https://doi.org/10.3390/rs18101673
Submission received: 19 April 2026 / Revised: 14 May 2026 / Accepted: 18 May 2026 / Published: 21 May 2026
Versions Notes

Abstract

Building change detection from bi-temporal remote-sensing imagery underpins urban planning, infrastructure monitoring, and disaster assessment. Existing deep-learning methods achieve high accuracy but rely on large parameter counts, while pixel-level supervision provides limited boundary guidance. We propose POCA-lite, a lightweight encoder–decoder with an inference-coupled geometry branch: three geometric prediction heads—distance transform, boundary, and center heatmap—whose outputs are fused back into the decoder via a feedback pathway active at both training and inference. On the LEVIR-CD benchmark under a unified retraining protocol, multi-seed evaluation shows that POCA-lite matches SNUNet in mean F1 while using 47% fewer parameters and 53% fewer FLOPs. Boundary F1 improves by 9.22 pp over the no-geometry baseline. Decomposition ablations reveal two complementary improvement sources: geometric supervision alone recovers 85% of the total gain, while the feedback fusion pathway recovers 92%; their combination achieves the full result. Geometry-aware targets outperform a generic multitask control. Cross-architecture transfer to SNUNet yields +1.06 pp F1. However, cross-dataset evaluation on WHU-CD shows that the method underperforms SNUNet on dense urban morphology, and zero-shot cross-dataset transfer is not established. These results indicate that inference-coupled geometric supervision is effective for lightweight, boundary-sensitive change detection on domains with well-separated building morphology, but its applicability is scope-bounded.
Keywords: change detection; remote sensing; lightweight network; boundary-aware supervision; auxiliary heads; feedback fusion; LEVIR-CD change detection; remote sensing; lightweight network; boundary-aware supervision; auxiliary heads; feedback fusion; LEVIR-CD

Share and Cite

MDPI and ACS Style

Shi, Y.; Yang, R.; Huang, B.; Gu, Z.; Lu, Y.; Yin, C.; Wen, Y.; Zhong, Y. POCA-Lite: A Lightweight Change-Detection Architecture with Geometry-Aware Auxiliary Supervision and Feedback Fusion. Remote Sens. 2026, 18, 1673. https://doi.org/10.3390/rs18101673

AMA Style

Shi Y, Yang R, Huang B, Gu Z, Lu Y, Yin C, Wen Y, Zhong Y. POCA-Lite: A Lightweight Change-Detection Architecture with Geometry-Aware Auxiliary Supervision and Feedback Fusion. Remote Sensing. 2026; 18(10):1673. https://doi.org/10.3390/rs18101673

Chicago/Turabian Style

Shi, Yongqi, Ruopeng Yang, Bo Huang, Zhaoyang Gu, Yiwei Lu, Changsheng Yin, Yongqi Wen, and Yihao Zhong. 2026. "POCA-Lite: A Lightweight Change-Detection Architecture with Geometry-Aware Auxiliary Supervision and Feedback Fusion" Remote Sensing 18, no. 10: 1673. https://doi.org/10.3390/rs18101673

APA Style

Shi, Y., Yang, R., Huang, B., Gu, Z., Lu, Y., Yin, C., Wen, Y., & Zhong, Y. (2026). POCA-Lite: A Lightweight Change-Detection Architecture with Geometry-Aware Auxiliary Supervision and Feedback Fusion. Remote Sensing, 18(10), 1673. https://doi.org/10.3390/rs18101673

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