Fixed-point image orthorectification algorithms for reduced computational cost

Date of Award

2016

Degree Name

Ph.D. in Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Eric John Balster

Abstract

Imaging systems have been applied to many new applications in recent years. With the advent of low-cost, low-power focal planes and more powerful, lower cost computers, remote sensing applications have become more wide spread. Many of these applications require some form of geolocation, especially when relative distances are desired. However, when greater global positional accuracy is needed, orthorectification becomes necessary. Orthorectification is the process of projecting an image onto a Digital Elevation Map (DEM), which removes terrain distortions and corrects the perspective distortion by changing the viewing angle to be perpendicular to the projection plane. Orthorectification is used in disaster tracking, landscape management, wildlife monitoring and many other applications. However, orthorectification is a computationally expensive process due to floating point operations and divisions in the algorithm. To reduce the computational cost of on-board processing, two novel algorithm modifications are proposed. One modification is projection utilizing fixed-point arithmetic. Fixed point arithmetic removes the floating point operations and reduces the processing time by operating only on integers. The second modification is replacement of the division inherent in projection with a multiplication of the inverse. The inverse must operate iteratively. Therefore, the inverse is replaced with a linear approximation. As a result of these modifications, the processing time of projection is reduced by a factor of 1.3x with an average pixel position error of 0.2% of a pixel size for 128-bit integer processing and over 4x with an average pixel position error of less than 13% of a pixel size for a 64-bit integer processing. A secondary inverse function approximation is also developed that replaces the linear approximation with a quadratic. The quadratic approximation produces a more accurate approximation of the inverse, allowing for an integer multiplication calculation to be used in place of the traditional floating point division. This method increases the throughput of the orthorectification operation by 38% when compared to floating point processing. Additionally, this method improves the accuracy of the existing integer-based orthorectification algorithms in terms of average pixel distance, increasing the accuracy of the algorithm by more than 5x. The quadratic function reduces the pixel position error to 2% and is still 2.8x faster than the 128-bit floating point algorithm.

Keywords

Digital elevation models Computer programs, Aerial photogrammetry, Image processing Digital techniques, Electrical Engineering, Engineering, Orthorectification, Fixed-Point Arithmetic, Image Processing, Aerial Imagery

Rights Statement

Copyright © 2016, author

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