Abstract
Higher spatial resolution imaging data are considered desirable in many Earth observation applications. In this work, we propose and demonstrate the TARSGAN (learning Terrestrial image deblurring using Adaptive weighted dense Residual Super-resolution Generative Adversarial Network) system for Super-resolution Restoration (SRR) of 10 m/pixel Sentinel-2 “true” colour images as well as all the other multispectral bands. In parallel, the ELF (automated image Edge detection and measurements of edge spread function, Line spread function, and Full width at half maximum) system is proposed to achieve automated and precise assessments of the effective resolutions of the input and SRR images. Subsequent ELF measurements of the TARSGAN SRR results suggest an averaged effective resolution enhancement factor of about 2.91 times (equivalent to ~3.44 m/pixel for the 10 m/pixel bands) given a nominal SRR upscaling factor of 4 times. Several examples are provided for different types of scenes from urban landscapes to agricultural scenes and sea-ice floes.
Highlights
Very high spatial resolution imaging data play an important role in many fields of Earth Observation (EO) applications, such as precision agriculture, forestry, urban planning, city intelligence, cartography, geology, oceanography, and energy and utility maintenance. there are very high spatial resolution imaging sensors, e.g., the 31 cm/pixelDigital Globe® WorldView-3 images, the cost of such very high spatial resolution images is generally high, especially when and where large spatial-temporal volumes are required.On the other hand, while improvements in the spatial resolution are gaining priority in the design of new optical-electronic sensors onboard EO satellites, we still need to trade-off spatial resolution against spectral resolution, swath-width, signal-to-noise ratio of the sensor, launch mass, and requested telecommunications bandwidth
We further explore our in-house Multi-scale Adaptive weighted dense Residual SR GAN (MARSGAN) [27] model that was previously developed for Mars applications, using the Sentinel-2 10 m/pixel colour images and
Inspired by [28,29], we propose practical modifications of the loss function, training dataset, and network architecture of MARSGAN, which we call learning Terrestrial image deblurring with Adaptive weighted dense Residual SR
Summary
Very high spatial resolution imaging data play an important role in many fields of Earth Observation (EO) applications, such as precision agriculture, forestry, urban planning, city intelligence, cartography, geology, oceanography, and energy and utility maintenance. there are very high spatial resolution imaging sensors, e.g., the 31 cm/pixelDigital Globe® WorldView-3 images, the cost of such very high spatial resolution images is generally high, especially when and where large spatial-temporal volumes are required.On the other hand, while improvements in the spatial resolution are gaining priority in the design of new optical-electronic sensors onboard EO satellites, we still need to trade-off spatial resolution against spectral resolution, swath-width, signal-to-noise ratio of the sensor, launch mass, and requested telecommunications bandwidth. Very high spatial resolution imaging data play an important role in many fields of Earth Observation (EO) applications, such as precision agriculture, forestry, urban planning, city intelligence, cartography, geology, oceanography, and energy and utility maintenance. There are very high spatial resolution imaging sensors, e.g., the 31 cm/pixel. Using super-resolution restoration (SRR/SR) to enhance existing EO data, especially those open access data, such as the European Space Agency’s Copernicus Sentinel systems, is becoming an increasingly attractive alternative, especially if the resultant products can be employed to derive higher spatial resolution products like reflectance and derivatives of reflectance. SRR refers to the process of enhancing (or increasing) the spatial resolution of images (or video frames) by exploiting non-redundant information from a set of repeat observations, or through a deep learning-based training and inference process. The growing technology interest in SRR, over the past 20 years, has led to the development and subsequent applications of many new algorithms, networks, and/or optimisations [1,2,3,4]
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