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The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

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Abstract

Unmanned aerial systems (UAS) have been used as a robust tool for agricultural and environmental applications in recent years. Remote sensing systems based on UAS typically acquire massive hyper-spatial images in its short turnaround. This paper takes advantage of graphics processing unit (GPU) massive parallel computation in order to process the huge data timely and efficiently. More specifically, this paper presents an index array approach for lens distortion correction and geo-referencing. They are the two essential components in UAS hyper-spatial image processing. The index array approach is also capable of parallelizing image file I/O and the orthoimage generation. In addition, this paper presents the dual tiled similarity algorithm for the image co-registration. The index array approach and the dual tiled similarity algorithm were evaluated using two UAS remote sensing datasets of South Padre island shorelines. The results show that this index array approach was able to speed up at least 10 times the lens distortion correction and the geo-referencing relative to the central processing unit (CPU) computation. This dual tiled algorithm could provide 12 times speedup compared with the CPU similarity computation.

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Acknowledgments

This work was supported partly by the NSF grant MRI: Acquisition of a High Performance Computing Cluster to Support Multidisciplinary Big Data Analysis and Modeling (#1429518).

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Authors and Affiliations

  1. Harte Research Institute for Gulf of Mexico Studies, Texas A&M University-Corpus Christi, Corpus Christi, TX, USA

    Lihong Su & James Gibeaut

  2. School of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX, USA

    Yuxia Huang & Longzhuang Li

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  1. Lihong Su
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  2. Yuxia Huang
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  4. Longzhuang Li
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Correspondence to Lihong Su.

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Su, L., Huang, Y., Gibeaut, J. et al. The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing. Geoinformatica 20, 859–878 (2016). https://doi.org/10.1007/s10707-016-0253-2

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  • DOI: https://doi.org/10.1007/s10707-016-0253-2

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