Aerospace and Electronic Systems Magazine June 2017 - 24

Feature Article:

DOI. No. 10.1109/MAES.2017.150249

Elimination of Resampling Errors in Wide Area Motion
Imagery (WAMI)
Curtis Cohenour, Ohio University Avionics Engineering Center, Athens, OH
Todd Rovito, WPAFB, Dayton, OH
Frank van Graas, Ohio University Avionics Engineering Center, Athens, OH

INTRODUCTION
Extraction of useful information from Wide Area Motion Imagery
(WAMI) data is limited by the resolution and clarity of the image.
The resolution of the raw image is defined by the camera, optics,
and conversion electronics. For use by an analyst, the raw image
must be converted to a geo-rectified image. This projection process
requires resampling, which results in a loss of resolution. In addition, compression may be required to accommodate a limited band
width transmission link or limited storage space, again with a loss
of resolution. Resampling and compression can reduce the clarity of
the image as seen by the analyst. In this article, the impact of resampling and compression for the Wright Patterson Air Force Base
(WPAFB) 20091021 data set are analyzed. This data set contains
both raw images and projected images. The projected images are
generated using the traditional sensor exploitation tool (SET) model.
The raw images are then projected using the SET model developed
here. The resolution of the traditional SET and the proposed SET are
measured. Measurement of the compression loss shows that compression is not responsible for the loss of resolution. The resolution
is limited by the resampling process. To eliminate resampling errors,
a novel SET model is proposed. The proposed SET model preserves
the resolution of the original raw image by eliminating the resampling process before data storage and transmission.
Conversion of a rectangular raw image to a trapezoidal georectified image requires two steps [1]. First, the proper warping
function must be determined that will convert the digital image to
the desired geometric coordinates. The warping function may take
the form of a homography. The homography may be different for
each element of the elevation map. Second, the digital image intensity values must be resampled to the coordinates of the desired
geometric coordinate system.

Authors' current addresses: C. Cohenour, F. van Graas, Ohio
University Avionics Engineering Center, EE, Stocker Center,
Athens, OH, USA 45710, E-mail: (cohenour@ohio.edu); T.
Rovito, WPAFB, Building 620, 2241 Avionics Circle, Dayton,
OH, USA 45433.
Manuscript received November 4, 2015, revised March 25,
2016, August 2, 2016, and ready for publication August 3, 2016.
Review handled by M. Greco.
0885/8985/17/$26.00 © 2017 IEEE
24

The first step includes rotation, scaling, translation, affine, and
projective transformations. These operations must be computed for
each tile of the terrain elevation database. The conversion affects
the location of the source image pixels in the destination image.
In the second step, the intensity of the destination image pixels
must be computed based on the coordinate conversions, and intensity of the source image pixels. The source image pixel locations
are integer values, while the destination image pixels are floating
point numbers. For example, the raw image pixel located at (200,
1,000), may be located at (42.6031, -3.7841) in the destination image. The process of assigning an intensity value to the destination
pixels based on the source image pixels is known as resampling.
Resampling is required because the source image pixels are not in
one-to-one correspondence with the destination image pixels [2].
Resampling is performed using nearest neighbor, bilinear, or
cubic spline interpolation [3]. The various methods of resampling
are compared in [4]. Cubic spline interpolation for both decimation
(zooming out), and interpolation (zooming in) are detailed in [1].
The destination images are combined with location information and provided to the end users as a projected, and geo-registered image. This is the standard model for SETs [5].
The resampling process causes a reduction in resolution [6]. The
images may be compressed to reduce the communication bandwidth
needed to deliver the image, or to reduce storage space. There may be
additional loss of resolution due to compression of the images.
This loss of resolution can result in reduced capability of trackers, or the need for more complex trackers [7]. In addition, the
value of the information extracted by an analyst may be reduced.

Figure 1.

Traditional SET model (top) and revised SET model (bottom).

IEEE A&E SYSTEMS MAGAZINE

JUNE 2017



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