IEEE Geoscience and Remote Sensing Magazine - December 2021 - 83

more small false alarms. The fusion of the MBI and
other features, e.g., the Harris detector [Figure 13(f)] and
spectral and shape features [Figure 13(h)], can reduce
these errors. These results illustrate that effective feature
N
W
S
2005
2017
Residential
Commercial
Unused Land
Breeding Surface
(a)
Infrastructure
Grassland
Industrial
Woodland
Water
Farmland
100
80
60
40
20
96.9
84.6
79.2
74.6
69.7
63
77.2 78.9
88.9
97.1
E
04 816Km
representation is the key to achieving good performance
for VHR change detection.
Apart from 2D characteristics, 3D information has
been exploited for building change detection in recent
2005
2017
CH LBP SIFT
CH + LBP + SIFT
Deep Learning
(b)
FIGURE 12. Land use change detection in the city of Shenzhen using high-spatial-resolution satellite imagery from 2005 to 2017, including
(a) land use maps and (b) an accuracy assessment with different features [144].
(a)
(b)
(c)
(d)
(e)
Unchanged
Changed
(f)
(g)
(h)
(i)
FIGURE 13. Building change detection maps obtained by different algorithms: (a) image (t1), (b) image (t2), (c) the reference change map,
(d) the MBI-SVM, (e) object-based CVA, (f) the MBI and the Harris detector, (g) the MBI-CVA, (h) the fusion of the MBI with spectral and
shape features, and (i) CVA using morphological features [51], [52].
DECEMBER 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
83
Overall Accuracy (%)

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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