Research Article

GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing

Volume: 8 July 3, 2026

GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing

Abstract

The Satellite imagery serves as a cornerstone for various human endeavours including environmental monitoring, urban planning, and disaster management. However, the efficacy of these images heavily relies on their quality, with atmospheric phenomena like haze posing significant challenges to detailed analysis. Addressing this issue is crucial for extracting meaningful insights. This research undertakes the task of analyzing current methodologies and proposes leveraging a new algorithm GATE-Dehaze, GAN-Attention-Transformer-Ensemble for dehazing, which consists of an adaptive ensemble algorithm applied on the output of four GAN based models that includes: a standard U-Net architecture, a Transformer-enhanced U-Net, a Transformer U-Net with CBAM integration and a Transformer U-Net with channel attention. GATE-Dehaze is used here for image-to-image translation, facilitating the conversion of source images (which are hazy) into clearer (dehazed) target images. The evaluation of our method is conducted using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores. On benchmarking datasets like SateHaze1k with dense haze, our model performed better than other SOTA models. Our proposed algorithm increased the SSIM score between ground truth and dehazed image from 0.9061 to 0.9257 on thin haze and 0.9264 to 0.9516 on moderate haze. By employing our approach, the aim is to generate dehazed satellite images that enable more precise analysis of the Earth's surface across varying spatial and temporal scales. Ultimately, the availability of high-quality, dehazed satellite imagery holds the potential to unlock invaluable insights that can inform decision-making processes in diverse fields ranging from environmental conservation to urban development and disaster response.

Keywords

References

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Details

Primary Language

English

Subjects

Image Processing

Journal Section

Research Article

Publication Date

July 3, 2026

Submission Date

December 11, 2025

Acceptance Date

March 21, 2026

Published in Issue

Year 2026 Volume: 8

APA
Gupta, I., Rawal, Y., Gupta, S., & Alegavi, S. (2026). GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing. Turkish Journal of Remote Sensing, 8. https://doi.org/10.51489/tuzal.1828786
AMA
1.Gupta I, Rawal Y, Gupta S, Alegavi S. GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing. TJRS. 2026;8. doi:10.51489/tuzal.1828786
Chicago
Gupta, Ishaan, Yesha Rawal, Shiwani Gupta, and Sujata Alegavi. 2026. “GATE-Dehaze: A GAN-Attention-Transformer Ensemble for Satellite Image Dehazing”. Turkish Journal of Remote Sensing 8 (July). https://doi.org/10.51489/tuzal.1828786.
EndNote
Gupta I, Rawal Y, Gupta S, Alegavi S (July 1, 2026) GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing. Turkish Journal of Remote Sensing 8
IEEE
[1]I. Gupta, Y. Rawal, S. Gupta, and S. Alegavi, “GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing”, TJRS, vol. 8, July 2026, doi: 10.51489/tuzal.1828786.
ISNAD
Gupta, Ishaan - Rawal, Yesha - Gupta, Shiwani - Alegavi, Sujata. “GATE-Dehaze: A GAN-Attention-Transformer Ensemble for Satellite Image Dehazing”. Turkish Journal of Remote Sensing 8 (July 1, 2026). https://doi.org/10.51489/tuzal.1828786.
JAMA
1.Gupta I, Rawal Y, Gupta S, Alegavi S. GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing. TJRS. 2026;8. doi:10.51489/tuzal.1828786.
MLA
Gupta, Ishaan, et al. “GATE-Dehaze: A GAN-Attention-Transformer Ensemble for Satellite Image Dehazing”. Turkish Journal of Remote Sensing, vol. 8, July 2026, doi:10.51489/tuzal.1828786.
Vancouver
1.Ishaan Gupta, Yesha Rawal, Shiwani Gupta, Sujata Alegavi. GATE-Dehaze: A GAN-attention-transformer ensemble for satellite image dehazing. TJRS. 2026 Jul. 1;8. doi:10.51489/tuzal.1828786

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