IEEE Geoscience and Remote Sensing Magazine - December 2021 - 75

Object-based CVA results [85] derived from different multitemporal
segmentation strategies are presented in Figure 6,
where it can be observed that different multitemporal segmentation
strategies can significantly affect change detection
results.
Generally speaking, three levels of object-based features
can be used for change detection [86]. In the first, the objectbased
features include the radiometry, geometry, and texture
for each image object [87]. For instance, in [88], key points of
each object are extracted in change detection, which was successfully
applied in three landslide scenes and one view that
examined land use changes. Bovolo [89] computed the mean
values of texture measures in separate parcels for change
detection, and better accuracy with high fidelity in the homogeneous
and border regions was achieved by the objectbased
method than with the pixel-based one. However, in
these studies, texture is still extracted in a pixel-based manner
and depends on the size of a moving window (or kernel).
More importantly, kernel- and window-based texture can
create between-class texture, leading to an edge effect [87].
Therefore, object-oriented texture computed within the
boundary of an object is recommended, such as object-wise
GLCM texture measures [87] and object-based MPs [90].
The second-level object-based features exploit relationships
between two image objects, e.g., adjacency, proximity,
and relations between neighboring objects [87]. For example,
Liang et al. [91] considered the relations of neighboring
objects in feature extraction for object-oriented change
detection. Yu et al. [92] combined a relative border with a
" forest with no change " and the normalized difference vegetation
index (NDVI) to identify the category of " change
from forest to developed land. " The third-level features refer
to spatial arrangements among multiple objects [87]. Thirdlevel
object-based features have been used in image classification,
such as urban functional zone extraction [93] and
urban village detection [94]. Nevertheless, such features
have rarely been used in VHR image change detection. In
[95], spatial dependency and sharing boundaries among
multiple objects are considered to reduce spurious errors
caused by shadow in urban vegetation change detection.
(a)
(b)
(c)
(d)
(e)
ωnc ωc1 ωc2 ωc3
Bounding Box Denoting Changes
FIGURE 5. Change detection results for QuickBird bi-temporal images: (a) image (t1), (b) image (t2), (c) the reference change map, (d) multiclass
deep CVA, (e) binary change deep CVA, and (f) object-based CVA [67].
DECEMBER 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
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(f)

IEEE Geoscience and Remote Sensing Magazine - December 2021

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