IEEE Geoscience and Remote Sensing Magazine - December 2021 - 71

METHODS
Change detection methods for VHR images are commonly
based on two steps: 1) feature extraction and 2) change detection
(see Figure 1).
FEATURE EXTRACTION
Change detection methods rely on effective multitemporal
feature representation to indicate whether and what changes
have occurred. It has been agreed that spectral-based methods
become ineffective in dealing with the challenges facing
VHR change detection. During the past decades, a large
number of image features have been extracted, which can
compensate for the limited spectral information contained
in VHR images and improve the discriminative capability
of image change information. In this review, image features
designed for VHR change detection are divided into the following
categories: textural, deep, object based, and angular
(Figure 3). These are potentially useful for dealing with
the challenges of limited spectral information and intrinsic
spectral variability. A summary of the major features used
for VHR change detection, including categories, subcategories,
descriptions, characteristics, most-used sensors, and
corresponding references, is presented in Table 2.
TEXTURAL FEATURES
Textural features depict contextual and structural information
by using a moving window or kernel, where the parameters
of size, direction, and distance must be appropriately
determined [5], [38]. Textural features for VHR change detection
can be categorized as statistical, structural, model
based, and transform based. Statistical textures describe
the relationships between the gray levels of local windows,
e.g., the gray-level cooccurrence matrix (GLCM); local binary
patterns (LBPs); and pixel shape index (PSI). The GLCM,
the most popular statistical texture, measures the contrast
(e.g., dissimilarity and homogeneity), orderliness (e.g., the
11 56
45 65
65 12
23 56
123456
110010
001000
000010
000000
100003
Statistical
Code
Convolution
Pooling
Convolution
Pooling
Fully
Connected
Closings Openings
00020
Structural
(a)
Model Based
Transform Based
Encoder
Decoder
Autoencoder
(b)
Single Object
Radiometry
Geometry
Texture
First Level
Stereo Photogrammetry
Forward
Nadir
Backward
Two Objects
Adjacency
Proximity
Relations
Second Level
(c)
Third Level
Angular Variation
Multiple Objects
Spatial
Arrangements
Convolutional Neural Network
DSM
ADF
Implicit
(d)
Explicit
FIGURE 3. Features for change detection using high-spatial-resolution remote sensing images. (a) Textural features. (b) Deep features.
(c) Object-based features. (d) Angular features. ADF: angular difference feature.
DECEMBER 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
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IEEE Geoscience and Remote Sensing Magazine - December 2021

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2021

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