Comparing ASM and Learning-Based Methods for Satellite Image Dehazing

Authors

DOI:

https://doi.org/10.21609/jiki.v18i2.1521

Abstract

Recent advancements in optical satellite technologies have significantly improved image resolution, providing more detailed information about Earth's surface. However, atmospheric interference, such as haze, is still a major factor in image capture. The interference results in visibility degradation of the acquired images, hindering computer vision tasks. Numerous studies have proposed various methods to recover haze-affected regions in satellite images, highlighting the need for more effective solutions. Motivated by this, this paper compares different atmospheric dehazing methods, including Atmospheric Scattering Model (ASM)-based and deep learning-based. The results show that SRD is the best ASM-based method, with a PSNR value of 19.09 dB and an SSIM of 0.908. Among deep learning models, DW-GAN achieves the best restoration results with a PSNR value of 26.22 dB and an SSIM of 0.959. SRD offers faster inference times, but still suffers from residual haze and noticeable color degradation compared to DW-GAN. In contrast, DW-GAN provides a more complete haze removal at the cost of higher computational demands than ASM-based methods.

Author Biographies

Steven Christ Pinantyo Arwidarasto, Faculty of Computer Science, University of Indonesia

received his B.Eng degree from University of Pancasila, Indonesia in 2023, majoring in Informatics Engineering. He is currently pursuing his master’s degree at Faculty of Computer Science, University of Indonesia. His research interests include image restoration, remote sensing segmentation, recommendation systems, and data science.

Laksmita Rahadianti, Faculty of Computer Science, University of Indonesia

received her B.CS. degree from University of Indonesia in 2009, her M.Sc. degree from Erasmus Mundus Color in Informatics and Media Technology master’s program from the University Saint
Étienne, France, University of Granada, Spain, and Gjøvik University College (now NTNU–
Norwegian University of Science and Technology) in 2012, and her Ph.D. degree in Computer Science and Engineering from Nagoya Institute of Technology, Japan, in 2018. Since 2021, she has been an assistant professor at Faculty of Computer Science, Universitas Indonesia. Her research interests include computer vision, photometric image analysis, image restoration, and holistic image quality. Dr. Rahadianti has handled various research projects and supervised multiple student theses in various fields of machine learning and computer vision.

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Published

2025-06-26

How to Cite

Steven Christ Pinantyo Arwidarasto, & Rahadianti, L. (2025). Comparing ASM and Learning-Based Methods for Satellite Image Dehazing. Jurnal Ilmu Komputer Dan Informasi, 18(2), 229–238. https://doi.org/10.21609/jiki.v18i2.1521

Issue

Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)

Section

Articles

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