IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 27

Oveis et al.
Figure 9.
SAR segmentation architecture using Wavelet pooling layers, proposed in [112].
Duan et al. [115] proposed a SAR Image segmentation
method based on convolutional-wavelet neural network.
They employed wavelet transform for pooling layers. In
other words, a wavelet-constrained pooling layer is
designed to substitute the conventional pooling in a CNN.
Since conventional pooling layers in CNNs blindly take a
maximum or average value from a rectangular window
and this may lose the information of edges and corners,
they employed wavelet transform for a more efficient
pooling scheme. In fact, average brightness information is
represented by low-frequency wavelet coefficients, and
the textures and the edge information can be deduced by
high-frequency ones. Their model is illustrated in
Figure 9.
CHANGE DETECTION
The main aim of change detection is to distinguish
changes between images that are taken from the same
scene but at different times. The models for change detection
typically take image pairs as input. Change detection
is similar to a classification problem where pixels are
divided into changed and unchanged categories. Change
detection in general can be used for disaster monitoring
[116], forest monitoring [117], video surveillance [118],
etc.
Asokan et al. [119] reviewed the various change
detection techniques in RS. Liu et al. [120] proposed a
symmetric convolutional network for change detection
between two input images, i.e., an optical and a SAR
image with the same size. Gong et al. [121] proposed a
change detection architecture by combining CNN and
SAE for detecting changes from bi-temporal SAR images.
They extracted features from the difference images by
SAE and then these features were clustered into three classes
as the pseudo labels (unchanged category, positive
change, and negative change) for training CNN. The result
of their ternary change detection is depicted in Figure 10.
Iino et al. [122] proposed a short-term urban change detection
method. They combined information contained in
SAR satellite images with digital surface models to
improve the accuracy of their suggested CNN-based network.
Cao et al. [123] proposed a CNN-based denoising
and change detection architecture for SAR images. Gao
et al. [124] employed a convolutional-wavelet neural network,
which was formally introduced in [115], for sea
ice change detection from SAR images. Employing
Figure 10.
Ternary change detection present in [118]. (a) Image acquired in 2003. (b) Image acquired in 2004. (c) Reference image.
MAY 2022
IEEE A&E SYSTEMS MAGAZINE
27

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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IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover3
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover4
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