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Journals >
Journal of Infrared and Millimeter Waves >
Volume 39 >
Issue 4 >
Page 505 > Article
Journal of Infrared and Millimeter Waves
Vol. 39, Issue 4, 505 (2020)
PolSAR image classification based on object-oriented technology
Yan XIAO
1
and Bin WANG
2
Author Affiliations
1
College of Exploration and Surveying Engineering, Changchun Institute of Technology, Changchun3002, China
2
Changchun Institute of Surveying and Mapping, Changchun13001, China
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DOI:
10.11972/j.issn.1001-9014.2020.04.015
Cite this Article
Yan XIAO, Bin WANG.
PolSAR image classification based on object-oriented technology
[J]. Journal of Infrared and Millimeter Waves, 2020, 39(4): 505
Copy Citation Text
EndNote(RIS)
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Fig. 1.
Location map of study area
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Fig. 2.
RADARSAT-2 image (Pauli RGB composition).
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Fig. 3.
Distribution map of samples for each class
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Fig. 4.
Comparison of accuracies for classifications
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Fig. 5.
Land-use maps based on different classification methods
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分解方法
极化参数
Pauli
Pauli_ a
Pauli_ b
Pauli_ c
Krogager
Krogager_ KS
Krogager_KH
Krogager_KD
Huynen
Huynen_T
11
Huynen_T
22
Huynen_T
33
Barnes1
Barnes1_T
11
Barnes1_T
22
Barnes1_T
33
Barnes2
Barnes2_T
11
Barnes2_T
22
Barnes2_T
33
Cloude
Cloude_T
11
Cloude_T
22
Cloude_T
33
H/A/α
H/A/α_T
11
H/A/α_T
22
H/A/α_T
33
Entropy(H)
SERD
RVI
DERD
PolarizationAsymmetry(PA)
ShannonEntropy (SE)
PedestalHeight (PH)
PolarizationFraction (PF)
Anisotropy(A)
Freeman2
Freeman2_Vol
Freeman2_Ground
Freeman3
Freeman_Vol
Freeman_Odd
Freeman_Dbl
Yamaguchi3
Yamaguchi3_Vol
Yamaguchi3_Odd
Yamaguchi3_Dbl
Yamaguchi4
Yamaguchi4_Vol
Yamaguchi4_Odd
Yamaguchi4_Dbl
Yamaguchi4_Hlx
Neumann
Neumann_delta_mod
Neumann_delta_pha
Touzi
TSVM_alpha_s
TSVM_alpha_s1
TSVM_alpha_s2
TSVM_alpha_s3
TSVM_tau_m
TSVM_tau_m1
TSVM_tau_m2
TSVM_tau_m3
Holm1
Holm1_T
11
Holm1_T
22
Holm1_T
33
Holm2
Holm2_T
11
Holm2_T
22
Holm2_T
33
Van Zyl
VanZyl3_Vol
VanZyl3_Odd
VanZyl3_Dbl
Table 1.
极化分解方法及相应的极化参数
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水体
道路
居民地
耕地
林地
草地
总和
UA/(%)
水体
693295
5500
50127
0
721
2931
752574
92.12
道路
610
938013
80331
50388
60134
51158
1180634
79.45
居民地
1142
113522
2874381
28776
51145
5961
3074927
93.48
耕地
0
113578
169628
3908101
827790
1241
5020338
77.85
林地
491
125447
73537
180194
3124430
3524
3507353
89.08
草地
0
41578
6670
2464
2797
143533
197042
72.84
总和
695538
1337638
3254674
4169923
4067017
208078
PA/(%)
99.68
70.12
88.32
93.72
76.82
68.98
OA/(%)
85.06
Kappa
0.8006
Table 2.
分类结果的混淆矩阵
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总体精度/(%)
Kappa
耗时
本文方法
85.06
0.8006
17 min
Pauli+ReliefF-PSO_SVM+集成
62.52
0.5002
14 min
极化分解+FSO+集成
65.37
0.5376
14h13min
极化分解+ReliefF-PSO_SVM
83.10
0.7747
9 min
Table 3.
不同分类方法的分类精度对比
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Abstract
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Figures&Tables (8)
Equations (4)
References (12)
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Yan XIAO, Bin WANG.
PolSAR image classification based on object-oriented technology
[J]. Journal of Infrared and Millimeter Waves, 2020, 39(4): 505
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Paper Information
Category: Remote Sensing Technology and Application
Received: Nov. 13, 2019
Accepted: --
Published Online: Sep. 17, 2020
The Author Email:
DOI:
10.11972/j.issn.1001-9014.2020.04.015
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