Robust Spatial-Domain Based Super-Resolution Mosaicing of CubeSat Video Frames: Algorithm and Evaluation

Debabrata Ghosh,Ronald A Fevig,Naima Kaabouch

doi:10.5539/cis.v7n2p68

Abstract

In this paper, a spatial domain based super-resolution mosaicing system and its evaluation results are presented. This algorithm incorporates two main algorithms, an image mosaicing algorithm and a super-resolution-reconstruction algorithm. Huber-based prior information is used to address the ill-posed nature of the super resolution problem. To test the efficiency of the proposed algorithm, four performance metrics are used: mean square error, peak signal-to-noise ratio, singular value decomposition based measure, and cumulative probability of blur detection. Testing is performed using datasets generated from both a high altitude balloon and an Unmanned Aerial Vehicle (UAV). Results show that the proposed algorithm is highly efficient in real-time applications, such as remote sensing on a small satellite (i.e., CubeSat.) Furthermore, the performance metrics are proven to be accurate in quantitative evaluation.

Highlights

There is a push within the spacecraft industry to move toward smaller satellites while preserving performance
Results show that the proposed algorithm is highly efficient in real-time applications, such as remote sensing on a small satellite (i.e., CubeSat.) the performance metrics are proven to be accurate in quantitative evaluation
We propose a super-resolution mosaicing algorithm which encompasses both the important aspects of such a system, alignment of the image frames in the sequence into a common coordinate system, and reconstruction of a high resolution (HR) image from those aligned low resolution (LR) frames

Summary

Introduction

There is a push within the spacecraft industry to move toward smaller satellites while preserving performance. There is an increased usage of the excess available mass that can be injected into an orbit that would otherwise be unutilized by a primary spacecraft. A 1-U CubeSat has a volume of 10 cm × 10 cm × 11 cm and a mass of 1.33 kg or less These constraints impose severe limits on payload elements, especially imaging systems whose field of view and resolution are dictated by physical dimensions that are fixed for a given remote sensing application. Some of these limitations can be overcome by employing computationally intensive image processing algorithms. We have initiated a new line of research at the University of North Dakota which will culminate in applying efficient super-resolution and mosaicing techniques to images acquired onboard a 1-U CubeSat in order to improve the resolution of those images

Methods

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Discussion

Conclusion