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Information × Registration Number 2123U011194, Article popup.category Препринт Title Comparison of Parameter reduction methods for Change Detection in Satellite Imagery (AI translated) popup.author Muliarska YanaMuliarska Yana popup.publication 01-01-2023 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/4410 popup.publisher Description Change Detection is a critical problem in Computer Vision with applications in various domains such as medical detection, satellite imagery, quality control, and traffic analysis. However, existing change detection models often have many pa- rameters, making them computationally expensive and challenging to implement in real-world applications. This study focuses on reducing the parameters set for the models designed explicitly for Change Detection in Satellite Imagery. These models typically process large-scale images, which can demand significant mem- ory resources and take considerable time to compute. As a solution, we implement three approaches, evaluate and compare their performance on a toy CNN model and an advanced SNUNet-CD model [9], designed for the Change Detection task. The highest parameter reduction rate we achieved for SNUNet-CD is 10.4% (1.25 million parameters) with only a 3.7% model accuracy drop. The experiments demonstrate that, when utilizing our methods, SNUNet-CD outperforms several SOTA models in the change detection domain. We succeeded in surpassing UNet++_MSOF [22] with respect to parameter count, while the original SNUNet-CD with 32 channels was unable to do so. The code implementation of this work is available on GitHub: https://github. com/muliarska/parameter-reduction-for-change-detection/. popup.nrat_date 2025-11-05 Close