Aerospace and Electronic Systems Magazine July 2017 Tutorial XI - 3

as well as from small stationary objects whose spectrum changes
from one image to another, as in the case of camouflage, concealment, and deception. This kind of change detection problem,
which is known in the literature as anomalous change detection
(ACD), has been deeply investigated [48], [55] in both airborne
and terrestrial applications [36], [46].
The ACD is formulated as a binary decision problem where one
has to test the two competing hypotheses: H0 (no change occurred)
and H1 (change occurred) based on the observed vectors derived
from the radiance measured on each pixel of the two images.
Basically, the general process of ACD is best looked at as having two main steps. The first one is a preprocessing step, which
includes radiometric and geometric correction. These aspects are
mainly due to physical constraints associated to the acquisition process. In fact, when the emitted or reflected electromagnetic energy
is acquired by a sensor installed onboard an aircraft or spacecraft,
the collected energy does not coincide with the energy emitted or
reflected from the same portion of the scene observed at a different
time. This is mainly due to variation in atmospheric conditions, the
Sun's azimuth and elevation, and the presence of aerosols which
modify the observed energy. In order to make multitemporal images radiometrically comparable, the radiometric distortions have
to be compensated. Several algorithms have been presented in the
literature to cope with this problem, such as the chronochrome
(CC, [50]), which represents the optimal linear mean square error predictor, and the covariance equalization (CE, [50]), which
represents a suboptimal solution requiring less binding constraint
than the CC, both in their global and cluster based versions [13].
Geometric correction is concerned with the correspondence between homologous pixels of the multitemporal images, so that pixels of each image refer to the same portion of the scene. In the case
of aerial or satellite images, this operation is performed by transforming the geodetic or UTM coordinates available of each pixel
to image coordinates. Nevertheless, especially in airborne applications, perfect registration of the images is very difficult to achieve
because of the unavoidable onboard instrumentation errors. Thus,
a residual mis-registration error (RMRE) inevitably remains. It reduces the detection performance of pixel-based ACD algorithms
[35] and it has to be accounted for in designing the ACD strategy.
JULY 2017, Part II of II

Acronyms
AC

Anomalous Change

ACD

Anomalous Change Detection

AC-ROC

Anomaly Conditional experimental ROC

CC

Chronochrome

CE

Covariance Equalization

CRM

Change Reference Map

EC

Elliptically Contoured

FAR

False Alarm Rate

FoDC

Fraction of Detected Changes

FoDNA

Fraction of Detected Nontemporal Anomalies

GLRT

Generalized Likelihood Ratio Test

HACD

Hyperbolic Anomalous Change Detector

HSI

Hyperspectral Imagery

LCRA

Local CoRegistration Adjustment

LR

Likelihood Ratio

ML

Maximum Likelihood

NARM

Nontemporal Anomaly Reference Map

PDF

Probability Density Function

RE

Radiometric Equalization

RGB

Red Green Blue

RMRE

Residual Mis-Registration Error

ROC

Object based experimental Receiver Operating
Characteristics

RX

Reed-Xiaoli anomaly detector

SACD

Straight Anomalous Change Detector

SDACD

Simple Difference Anomalous Change Detector

SDHACD

Simple Difference Hyperbolic Anomalous Change
Detector

SV

Single Vector

SVACD

Single Vector based Anomalous Change Detector

IEEE A&E SYSTEMS MAGAZINE

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