Aerospace and Electronic Systems Magazine July 2017 Tutorial XI - 14

Introductory View of Anomalous Change Detection
EX-2 makes use of images collected on
different days and under different acquisition conditions.
With reference to EX-1, Fig. 2 (a) and
(b) show the red green blue (RGB) representations of the test image (D1F12H2)
and the reference image (D1F12H1), respectively. In Fig. 2 (c), the CRM representing objects that are not present in the
reference image but appear in the test image is superimposed (red plots) to the RGB
of the test image. The CRM contains 21
small size changes corresponding to manmade objects (basically vehicles and tarps)
that occupy a few pixels (<20) in the test
image. Fig. 2 (c) also shows (blue plots)
the nontemporal anomalies reference map
(NARM), including small size anomalous
objects that can be observed in both the
test and the reference image. The anomalies have been carefully obtained by visual
inspection with the aid of the information
recorded during the data acquisition and
the results provided by the RX anomaly
detector. The total number of objects in
the NARM is 36. Though the NARM includes most of the anomalous objects in
the scene, it is not claimed to be exhaustive. It is introduced to discuss the ability
of the various algorithms in discriminating
temporal and nontemporal anomalies.
Fig. 2 (d) shows the sum of the RGB
representations of the test and reference
images and gives evidence of the presence
of RMRE in the considered image pair. In
Figure 2.
fact, comparing Fig. 2 (d) with Fig. 2 (a),
EX-1. (a) Test image D1F12H2. (b) Reference image D1F12H1. (c) CRM (red dots) and NARM
a sort of blurring effect due to the random
(blue dots). (d) Average of the test image and the reference image, the red rectangle points out a
misalignment between the two images can
region of the scene where the mis-registration between the two images is more evident.
be perceived in the first figure. Blurring is
more evident in the region (red rectangle)
on the top right side of Fig. 2 (d).
Similar
graphical
representations
are shown in Fig. 3 with reftwo images are assumed in turn as reference and test images. Each
erence to EX-2. Specifically, Fig. 3 (a) shows the RGB for the
CRM denotes the "insertion" changes involving those objects that
test image (D2F22H2). Fig. 3 (b) shows the RGB for the reference
are not present in the reference image but appear in the test image.
image (D1F12H1), that is the same as that in Fig. 2 (b). Fig. 3
More details about the measurement campaign and the shared
(c) shows the CRM (red plots) and the NARM (blue plots) superdata can be found in [6].
imposed to the RGB of the test image. The number of recorded
changes and that of the objects in NARM are both equal to 29.
EXPERIMENT DESCRIPTION
Fig. 3 (d) shows the sum of the RGB representations for the test
and the reference image. Note the blurring induced by the RMRE
Two different experiments are carried out; they are referred to as
that is particularly evident on the white calibration tarp located apEX-1 and EX-2, hereinafter. In both the experiments, the referproximately in the center of the scene. It is worth noting that, in
ence image is the one called D1F12H1, whereas the test images are
this case, due to the different acquisition and illumination condiD1F12H2 in EX-1 and D2F22H2 in EX-2.
tions, the test and the reference images have shadowed areas loEX-1 exemplifies the case of images acquired on the same
cated in different regions of the scene. As an example, consider the
day (with a time delay of approximately 15 min) and with very
shadowed areas within the red rectangles in Fig. 3 (a) that are not
similar illumination and atmospheric conditions. Conversely,
14

IEEE A&E SYSTEMS MAGAZINE

JULY 2017, Part II of II



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