IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 28

A Survey on the Applications of Convolutional Neural Networks for Synthetic Aperture Radar: Recent Advances
Figure 11.
Conceptual comparison between object detection and scene classification in SAR images. (a) Scene classification in [130]. (b) Object (ship)
detection using CNN in [131] where red and yellow rectangles show the detected and missed ships, respectively.
convolutional layers, Gao et al., [125] and [126], have also
addressed the change detection problem in SAR images,
by using a transfer learning-based multilevel fusion network
and a channel weighting-based cascade network,
respectively. Liu et al. [127] proposed a local-restricted
CNN change detector for POLSAR data. Since neighboring
pixels have similar characteristics, they formulated an
extra regularization term, called local restriction, in the
loss function and imposed it onto the output layer of their
CNN.
Dong et al. [128] proposed a modified version of
Siamese network for the problem of change detection
in SAR images. The Siamese network [129] known as
twin network, consists of two identical CNN-based
subnetworks where all the weights and biases of the
two subnetworks are tied together. The downside the
of the Siamese network is that it can easily suffer from
the interference of semantic distractors, in particular,
the background [130], since it ignores the information
of background [131]. The Siamese network can also be
used to identify corresponding patches in SAR and
optical images [132].
28
OBJECT DETECTION
It should be noted that SAR object detection is different
from SAR scene classification. Scene classification aims
at assigning only one label to a SAR image. On the contrary,
object detection aims at assigning multiple labels to
a SAR image where each label shows the location and the
type of an object with a bounding box. An example to
highlight the difference between scene classification [133]
and object detection [134] in SAR images is illustrated in
Figure 11.
Region-based CNNs (R-CNN) [135], Faster-RCNN
[136], You Only Look Once (YOLO) [137] and SSD (Single
Shot Multibox) [138] have been developed in the
recent years for object detection tasks [139]. YOLO is a 1stage-detector
and it is significantly faster than Faster R-
CNN since it performs region proposal and classification
simultaneously. However, the detection accuracy of Faster
R-CNN is better than that of YOLO. YOLO was implemented
in a way similar to the human object recognition
system [140]. Cui et al. [141] proposed a region-based
CNN-based framework to locate ground targets on large
IEEE A&E SYSTEMS MAGAZINE
MAY 2022

IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV

Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV

Contents
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Contents
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover2
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 1
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