Deep Super Resolution Techniques for Remote Sensing Big Data: A Comparative Study

Download PDF
Open Webpage

Rishabh Jain, Ranga Raju Vatsavai

Published: 2024, Last Modified: 05 Feb 2026IEEE Big Data 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Recent advancements in remote sensing technology have led to vast collections of big image data, ranging from high-resolution (sub-meter) to medium-resolution (10 to 30 meters). While medium-resolution images are freely available, high-resolution imagery is costly. Several applications, such as object recognition and cloud imputation, necessitate super-resolving medium-resolution images to align with high-resolution data. Deep learning has significantly improved single-image super-resolution (SISR) accuracy. However, super-resolving at scales exceeding 5x (10 meters to 2 or 1 meter) remains challenging. Unlike natural photographs, remote sensing images require recovering intricate details for 25 pixels at a super-resolution of 2 meters from an aggregated single pixel at a medium resolution of 10 meters. This demands the ability to discern subtle geographic features, such as textures, variations in vegetation and land use changes, and small man-made structures. Existing SR techniques, primarily designed and tested on natural images, focus on establishing hidden pixel relationships by learning contextual constraints from high-resolution data and employing innovative loss functions during high-resolution image reconstruction through residual or similarity transformations. However, their effectiveness with geospatial data requires careful evaluation. To evaluate the effectiveness of various super-resolution (SR) techniques, this paper conducts a comparative study using a diverse dataset of over 16,530 high-resolution satellite images from Planet Explorer’s remote sensing database, sourced from Sentinel-2 (10-meter) and RapidEye (3-meter) satellites. The performance of each method is assessed and compared using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. We hope the insights from this comparative study will help guide the selection of suitable SR methods by highlighting their advantages and disadvantages.