Stripe Error Correction for Landsat-7 Using Deep Learning - Adiyaman, Hilal; Varul, Yunus Emre; Bakirman, Tolga; Bayram, Buelent (SPRINGER INT PUBL AG, 2025)
Long-term time series satellite imagery became highly essential for analyzing earth cycles such as global warming, climate change, and urbanization. Landsat-7 satellite imagery plays a key role in this domain since it provides open-access data with expansive coverage and consistent temporal resolution for more than two decades. This paper addresses the challenge of stripe errors induced by Scan Line Corrector sensor malfunction in Landsat-7 ETM+ satellite imagery, resulting in data loss and degradation. To overcome this problem, we propose a Generative Adversarial Networks approach to fill the gaps in the Landsat-7 ETM+ panchromatic images. First, we introduce the YTU_STRIPE dataset, comprising Landsat-8 OLI panchromatic images with synthetically induced stripe errors, for model training and testing. Our results indicate sufficient performance of the Pix2Pix GAN for this purpose. We demonstrate the efficiency of our approach through systematic experimentation and evaluation using various accuracy metrics, including Peak Signal-to-Noise Ratio, Structural Similarity Index Measurement, Universal Image Quality Index, Correlation Coefficient, and Root Mean Square Error which were calculated as 38.5570, 0.9206, 0.7670, 0.7753 and 3.8212, respectively. Our findings suggest promising prospects for utilizing synthetic imagery from Landsat-8 OLI to mitigate stripe errors in Landsat-7 ETM+ SLC-off imagery, thereby enhancing image reconstruction efforts. The datasets and model weights generated in this study are publicly available for further research and development: https://github.com/ynsemrevrl/eliminating-stripe-errors.
Keywords: Landsat-7; Stripe error; Deep learning; GAN; Image enhancement; SLC-OFF IMAGERY; NEURAL-NETWORK; GAPS; ETM+; RECONSTRUCTION; RESTORATION; REGRESSION; NOISE; FILL; Remote Sensing; Imaging Science & Photographic Technology