Enhancing the Quality of Satellite Images in Disaster Management: A Comparative Analysis of Zero-DCE, CIDNet, and MIRNet Models

Abstract

Satellite images play a critical role in disaster management and rescue operations in natural disasters. However, these images create difficulties in analysis due to noise and loss of detail. This study evaluates the performance of deep learning models—Zero-DCE, CIDNet, and MIRNet—in enhancing low-light satellite images. The performances of the models were analyzed with metrics such as PSNR, SSIM and LPIPS using post-earthquake satellite images of the Hatay region. In addition, the performance of the models on a benchmark dataset was analyzed. The results showed that CIDNet was superior in detail and structural accuracy, while MIRNet was successful in color and brightness enhancement. Although Zero-DCE was effective in brightness enhancement, it lagged behind other models in structural accuracy. In this study, the potential of deep learning-based image enhancement models in disaster management and the image features on which they are effective were revealed.

Keywords

• Low-light image enhancement
• Deep learning
• Zero-DCE
• CIDNet
• MIRNet

Supporting Institution

Technological Research Council of Turkey (TUBITAK)

Project Number

123E669

Thanks

The satellite images from the February 6, 2023 earthquake used in this study were obtained from the Istanbul Technical University Satellite Communications and Remote Sensing Application and Research Center. We extend our gratitude to the center for providing the dataset for this research. Additionally, this study was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under project number 123E669. We gratefully acknowledge TÜBİTAK's support.

Doğal Afet Yönetiminde Uydu Görüntülerinin Kalitesinin İyileştirilmesi: Zero-DCE, CIDNet ve MIRNet Modellerinin Karşılaştırmalı Analizi

Abstract

Doğal afetlerde uydu görüntüleri, afet yönetimi ve kurtarma operasyonlarında kritik bir rol oynamaktadır. Ancak düşük ışık koşullarında elde edilen bu görüntüler, gürültü ve detay kaybı nedeniyle analizlerde zorluk yaratmaktadır. Bu çalışmada, düşük ışık uydu görüntülerinin iyileştirilmesinde Zero-DCE, CIDNet ve MIRNet modellerinin performansları değerlendirilmiştir. Hatay bölgesine ait deprem sonrası uydu görüntüleri kullanılarak modellerin PSNR, SSIM ve LPIPS gibi metriklerle performansları analiz edilmiştir. Ayrıca modellerin bir kıyaslama veri kümesindeki başarımı da analiz edilmiştir. Sonuçlar, CIDNet’in detay ve yapısal doğrulukta üstün olduğunu, MIRNet’in ise renk ve parlaklık iyileştirme konusunda başarılı olduğunu göstermiştir. Zero-DCE, parlaklık artırmada etkili olsa da yapısal doğrulukta diğer modellere göre geride kalmıştır. Bu çalışmada derin öğrenme tabanlı görüntü iyileştirme modellerinin afet yönetimindeki kullanım potansiyeli ve etkili oldukları görüntü özellikleri ortaya konulmuştur.

Keywords

• Düşük ışık görüntü iyileştirme
• Derin öğrenme
• Zero-DCE
• CIDNet
• MIRNet

Supporting Institution

TÜBİTAK

Project Number

123E669

Thanks

Bu çalışmada kullanılan 6 Şubat 2023 depremine ait uygu görüntüleri İstanbul Teknik Üniversitesi Uydu Haberleşme ve Uzaktan Algılama Uygulama Araştırma Merkezinden temin edilmiştir. Veri kümesinin araştırma için paylaştıklarından İstanbul Teknik Üniversitesi Uydu Haberleşme ve Uzaktan Algılama Uygulama Araştırma Merkezine teşekkür ederiz. Ayrıca bu çalışma Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 123E669 numaralı proje kapsamında desteklenmiştir. TÜBİTAK’ desteklerinden dolayı teşekkür ederiz.

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