Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Abstract

In recent years, we have witnessed significant development in the space sector, in particular regarding Earth imaging. Small satellites, whose size and construction make their production much cheaper, are becoming increasingly popular. As a result, a larger number of satellites may be placed in space, and thus, they may perform more frequent observations of selected spots on Earth. Unfortunately, the construction of these satellites also affects their observation capacity as they have a weaker spatial resolution. Scientists have been dealing with the problem of improving the spatial resolution of satellite imaging for many years. Numerous methods were developed that allow for the best possible representation of high-resolution images based on low-resolution images. However, the application of traditional solutions to improve the resolution of digital images requires an additional high-resolution image. As far as images obtained by small satellites (e.g., nano, micro, or mini) are concerned, the difference between the spatial resolution of panchromatic and multispectral images is small (e.g., for SkySat-3 – SkySat-15 satellites, it is only 0.16 m). The need to increase the spatial resolution of an image that does not have a corresponding higher resolution image (e.g., a panchromatic image or a sequence of images) causes additional problems. This article presents a review of the methods to improve the spatial resolution of small-satellite imaging. The authors analyze the interpolation, pansharpening, and digital image processing methods. Additionally, the article focuses on presenting solutions based on deep learning that enables the enhancement of the spatial resolution of images obtained from small satellites. The methodology of creating databases used for network training is described. Finally, the authors present the main limitations of the analyzed solutions and future development trends that will enable to improve the spatial resolution with the use of a single image.

Keywords

• Convolutional neural networks
• deep learning
• neural networks
• single image super-resolution (SISR)
• super- resolution