Abstract
We introduce a robust and light-weight multi-image super-resolution restoration (SRR) method and processing system, called OpTiGAN, using a combination of a multi-image maximum a posteriori approach and a deep learning approach. We show the advantages of using a combined two-stage SRR processing scheme for significantly reducing inference artefacts and improving effective resolution in comparison to other SRR techniques. We demonstrate the optimality of OpTiGAN for SRR of ultra-high-resolution satellite images and video frames from 31 cm/pixel WorldView-3, 75 cm/pixel Deimos-2 and 70 cm/pixel SkySat. Detailed qualitative and quantitative assessments are provided for the SRR results on a CEOS-WGCV-IVOS geo-calibration and validation site at Baotou, China, which features artificial permanent optical targets. Our measurements have shown a 3.69 times enhancement of effective resolution from 31 cm/pixel WorldView-3 imagery to 9 cm/pixel SRR.
Highlights
If the total number of pixels of such profile rises in the super-resolution restoration (SRR) image was artificial optical targets, at the Baotou Geocal site, provided broad dynamic compared against the total number of pixels involved in its lower resolution (LR) counterpart, for the same range, good uniformity, high stability and multi-function capabilities
The effective resolutions achieved from SRGAN, ESRGAN and MARSGAN alone, for ultra-high resolution satellite imagery, when a network did not have any prior knowledge of the higher resolution (HR) counterpart of the input images, were generally limited, as demonstrated in the slanted-edge measurements
OpTiGAN follows the two-stage processing framework used in MAGiGAN, using a traditional multi-image MAP approach (i.e., optical-flow PDE-TV (OFTV)) and a state-of-the-art deep learning-based approach (i.e., MARSGAN)
Summary
Increasing the spatial resolution of spaceborne imagery and video using groundbased processing, or, where feasible, onboard a smart satellite, allows greater amounts of information to be extracted about the scene content. Such processing is generally referred to as super-resolution restoration (SRR). SRR combines image information from repeat observations or continuous video frames and/or exploits information derived (learned) from different imaging sources, to generate images at much higher spatial resolution. Enhancing the ultra-high spatial resolution Earth observation (EO) images, or high definition (HD) videos, is an active driver for many applications in the fields of agriculture, forestry, energy and utility maintenance and urban geospatial intelligence. The ability to further improve 30 cm/80 cm EO images and videos into 10 cm/30 cm resolution SRR images and videos will allow artificial intelligence-based (AI-based) analytics to be performed in transformative ways
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