Aerospace and Electronic Systems Magazine July 2017 - 45

Lim, Gianelli, and Li
θˆini = arg  max  H FFT (U cos (θ ) ,U sin (θ ) ) ,
−90°≤θ < 90°

(20)

U

where HFFT (X,Y) is obtained by the 2-D Fourier transform of an
object h(x,y) and then nearest neighbor interpolation (NNI). The
preliminary pose angle estimate θˆini is then used as the center value
of the range θˆini − Δθ ,θˆini + Δθ for the application of the PST

[10], where Δθ is the interval of interest around θˆini.
In PST, the points obtained by bilinear interpolation (BI) in
frequency domain are denoted as HPST (X,Y). We then extract the
region θˆini − Δθ ,θˆini + Δθ as shown in Figure 8(b) (the green

rectangle). Finally, the projection of h(x,y) at θˆini − Δθ ,θˆini + Δθ  

is obtained by

)

)

)

∞

rθ ( u ) =  Rθ (U ) e+ jUu dU ,

(21)

−∞

where

(

)

Rθ (U ) = H PST U cos (θ ) ,U sin (θ ) .

(22)

After conducting 1-D inverse Fourier transform in (21), we find
the final estimated pose angle from the maximum value (pointed
out by a violet arrow) in the 2-D image on (θ,u) as shown in Figure
8(c).
As a final step the target images are rotated such that they are
vertically oriented. It should be noted that the pose estimation algorithm yields the angle between the parallel lines (see Figure 7)
and the horizontal axis. Figure 9 demonstrates successful rotation
of a target image using the hybrid pose estimation algorithm.

Figure 9.

A pose-corrected target chip (with 30% missing data).

FEATURE EXTRACTION
Following pose estimation, each target image is rotated to have
identical orientation. Once this has been completed, the data size of
each target image is reduced by generating the 1-D horizontal and
vertical cumulative sum vector (CSV) [12] as shown in Figure 10.
These CSVs are the features that are used in the following classification algorithm. The CSVs are obtained by summing the rows and
columns of the modulus image to vectors. The relevant equations
are given by
L

2

h r =  αˆ pr ,l ,   for  r = 1,..., R,
l =1

hˆ r =

hr
,
max ( h r )

Figure 10.

R

ture registration prior to classification. The VCSV in the Figure 11
is obtained from the wide-angle circular SAR image. In this SAR
image, the shadowing effect is almost eliminated by synthesizing
all subimages obtained from different locations of a radar platform.
As shown in Figure 11(a), the two peaks of the VCSV are used
to estimate the center of the VCSV profile, which is used as a reference point as shown in Figure 11(b).
The centers of the VCSVs for each target are aligned, and zeros
are affixed to the beginning and the end of the VCSVs to ensure
that they are the same length as shown in Figure 12.

Extracting a target of interest (in 30% missing data).

2

v l =  αˆ pr ,l ,    for  l = 1,..., L,
r =1

vˆ l =

vl
,
max ( v l )

(23)

where αˆ pr ,l is the reflection coefficient after the pose correction.
The boundaries of the target are determined by the two CSVs.
They are marked as blue dots as shown in Figure 10. This process
yields a smaller sized target chip (in terms of total data size) from
the original image. The vertical CSV (VCSV) is used for target feaJULY 2017

IEEE A&E SYSTEMS MAGAZINE

45



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