High-Confidence Clustering and Lightweight U-Net for SAR Change Detection

Ihmeida, Mohamed and Bian, Shaojun and Azad, R. Muhammad Atif and Saleh, Tamer (2025) High-Confidence Clustering and Lightweight U-Net for SAR Change Detection. In: Lecture Notes in Artificial Intelligence (LNAI). Springer. ISBN 9783032114020

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Abstract

Synthetic aperture radar (SAR) image change detection (CD) involves identifying changes between images captured at different times over the same geographical region. SAR provides significant advantages for disaster-related remote sensing due to its all-weather capabilities and ability to penetrate clouds and darkness. However, Synthetic aperture radar change detection under severe speckle is challenging yet essential for reliable all-weather monitoring. We introduce a hybrid framework that fuses classical and deep learning techniques: after edge-preserving denoising, three standard difference maps are combined into a single change indicator, on which a two-cluster Fuzzy C-Means selects high-confidence “changed” and “unchanged” pixels as training seeds. A compact three-level Mini-U-Net, trained with a weighted Binary Cross-Entropy and Dice loss, then refines the remaining uncertain areas via sliding-window inference. Experiments on three diverse SAR datasets demonstrate that our method achieves higher overall accuracy, F1 score, and IoU than state-of-the-art approaches, delivering robust speckle suppression and precise change delineation.

Item Type:	Book Section
Identification Number:	10.1007/978-3-032-11402-0_27
Dates:	Date Event 29 August 2025 Accepted 24 November 2025 Published Online
Uncontrolled Keywords:	SAR change detection, Fused difference image, U-net
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	22 Sep 2025 12:31
Last Modified:	02 Dec 2025 14:12
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16649

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