IEEE Geoscience and Remote Sensing Magazine - December 2021 - 125
dependence between two data sets and particularly suitable
for registration with different imaging mechanisms. However,
they are computationally expensive, which may be restrictive,
as remote sensing images are always relatively large.
FREQUENCY-DOMAIN APPROACHES
Frequency-domain approaches indirectly utilize intensity information,
transforming an image and exploiting its frequency-domain
features for registration. By so doing, they accelerate
the computational speed of relatively small geometric
dislocations. Fourier techniques are typical representations
of frequency-domain registration, which were first used to
register images with translational changes [48]. Phase-based
correlation approaches [23], [49]-[51] exploit the Fourier
transform to register images by searching for global optimal
matching [53]; they compute the cross-power spectra of the
sensed and reference images and seek the location of the
peak. The translational and rotational properties of the Fourier
transform are employed to calculate the transformation
parameters [53]. Frequency domain approaches are robust
against frequency-dependent noise and illumination changes.
They also contribute to the acceleration of computational
efficiency [54] since they neither involve feature extraction,
as feature-based approaches do, nor require an optimization
approach in the spatial domain, which would increase their
computational complexity [53]. However, given that the Fourier
transform offers poor spatial localization, the operation
can be replaced by a wavelet transform with strong spatial and
frequency localization [55], which can be applied to remote
sensing image registration [56]. Recently, phase congruency
(PC) has been used to represent structural information in
remote sensing images; it is similar to
the image gradient but is invariant in
terms of image contrast and brightness
variations [57], [58].
In short, most correlation-like approaches
are statistical similarity metrices
that do not facilitate structural
information or high computational
complexity. Owing to their easy hardware
implementations, they remain
in frequent use for registration evaluation
[59]. Fourier techniques have
some advantages in terms of computational
sufficiency, and they are
robust against frequency-dependent
noise. However, they have limitations
in the case of image pairs with significantly
different spectral content. Although
MI methods offer outstanding
performance compared with the
two aforementioned algorithms,
they do not always provide a global
maximum of the entire search space
for the transformation, as images
containing insufficient information
or the overlap between two scenes inevitably reduces their robustness
[16], [25]. Overall, intensity-based approaches directly
use the pixel value of an image, without error accumulation,
offering high-precision registration. However, these algorithms
have limitations in terms of large rotations, translations, scale
differences, and so on and are quite time-consuming.
OPTICAL FLOW ESTIMATION
Similar to the area-based approaches, optical flow estimation
calculates object motions with direct and indirect consistency
constraints based on pixel intensity. This technique is popular
in computer vision for motion estimation. Owing to the
similarity between the displacements of corresponding pixels
under the same coordinate system and the optical flow of an
object, some studies have utilized optical flow estimation to
register remote sensing images [60], [61]. Unlike area-based
approaches, optical flow estimation calculates pixel displacement
based on intensity and gradient consistency constraints
for coordinate recalculation. After resampling, the intensity
value is assigned to the new noninteger position, and the
aligned image is acquired [62], as summarized in Figure 7.
Optical flow is a 2D displacement field that describes the
apparent motion of brightness patterns between two successive
images [63], and its concept was proposed by Gibson
[64]. Horn and Schunck (HS) [63] and Lucas and Kanada
(LK) [65] proposed a differential approach for optical flow
calculation in 1981. Since then, many extensions and modifications
have been proposed for video image processing
[66]-[68]. Given that the process is at the initial stage of development
in the remote sensing field and that many studies
have focused on differential techniques, the following
Reference Image
Sensed Image
Displacement
Calculation
Pixels (Assumption)
u1 = u1 + ∆u1
′
v1 = v1 + ∆v1
′
Coordinate
Transformation
un = un + ∆un
′
vn = vn + ∆vn
′
FIGURE 7. Optical flow estimation for remote sensing image registration. [( ,) indicates the
pixel coordinates in the reference image, and (, )uv
uv
ij
TT
ij
ij
ll indicates the coordinates of the corresponding
pixel in the sensed image. The coordinate difference, which we called the displacement,
is depicted as (, ).]uv
DECEMBER 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
125
u = u′ + ∆u
v = v′ + ∆v
IEEE Geoscience and Remote Sensing Magazine - December 2021
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