采用深度学习的极化SAR地物分类方法综述

doi:10.19665/j.issn1001-2400.2023.03.015

Abstract

Abstract:

Polarimetric synthetic aperture radar (PolSAR) is one of the main sources of remote sensing data,because it can realize all-day and all-weather imaging.Terrain classification is an important research in the field of PolSAR data interpretation,which has become one of the hotspots in the research field and has been widely used in both military and civilian applications.In recent years,deep learning has achieved remarkable results in many research fields,some of which have been made in the field of PolSAR image processing.Compared with traditional image classification methods,the deep learning method has the advantages of automatic extracting deep features,strong generalization and high accuracy.In this paper,the existing terrain classification methods for the PolSAR image based on deep learning are reviewed.According to the different network models in deep learning,the research on PolSAR terrain classification is described in detail from three aspects,that is,deep belief network,sparse autoencoder network and convolutional neural network.Finally,the advantages and disadvantages of PolSAR terrain classification based deep learning are summarized in comparison with classical classification methods.Meanwhile,the development trend of PolSAR terrain classification is analyzed and discussed.

Key words: polarimetric synthetic aperture radar, image classification, deep learning, research review

CLC Number:

XIE Wen,HUA Wenqiang,JIAO Licheng,WANG Ruonan. Review on polarimetric SAR terrain classification methods using deep learning[J].Journal of Xidian University, 2023, 50(3): 151-170.

Figures/Tables 10

References 109

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种类	方法
种类	Wishart	SVM	DBN^[37]	WRBM^[40]	WBRBM^[41]	GRBM^[40]
Stem beans	93.36	84.06	76.76	94.78	96.60	95.65
Rapeseed	61.41	62.56	51.89	80.42	86.71	83.26
Bare soil	87.14	92.86	94.95	93.64	96.99	97.96
Potatoes	77.66	82.53	87.61	98.11	88.88	92.69
Beet	83.70	93.95	87.54	96.30	95.81	97.45
Wheat 2	73.61	71.67	81.03	94.66	88.17	82.19
Peas	86.52	91.38	91.44	92.07	96.80	95.97
Wheat 3	90.77	95.67	97.66	97.82	93.77	98.02
Lucerne	94.61	79.21	95.70	92.45	96.11	96.91
Barley	96.95	95.31	59.28	98.88	98.06	91.80
Wheat	95.17	90.23	94.34	93.86	92.32	98.14
Grasses	62.58	72.95	10.03	81.52	90.93	67.64
Forest	93.80	90.57	92.52	95.34	91.41	95.64
Water	40.33	90.99	68.95	99.74	99.60	89.59
Building	87.35	1.63	23.95	82.45	85.26	77.69
OA	81.66	79.70	74.24	92.80	93.08	90.71
Kappa	0.800 7	0.778 6	0.719 1	0.921 5	0.924 9	0.898 7

种类	方法
种类	DBN^[37]	WRBM^[40]	SAE^[44]	SAE- BWMRF^[45]	SSAE- SL^[48]	WAE^[54]	CV-WSAE^[55]	CAE^[62]	MAE^[56]
Stem beans	79.05	95.17	84.22	98.91	98.80	92.42	78.89	78.98	95.91
Rapeseed	39.33	64.31	74.19	66.49	87.82	78.80	72.90	83.32	84.11
Bare soil	0.00	39.95	93.78	99.88	97.73	96.06	76.70	79.24	92.62
Potatoes	93.74	87.00	81.55	97.33	93.74	93.70	96.30	98.48	89.64
Beet	93.25	89.33	88.96	98.79	96.52	89.06	94.37	96.72	95.77
Wheat 2	89.52	62.63	64.88	93.51	87.21	90.62	87.60	85.93	81.02
Peas	93.77	93.33	92.85	97.91	95.72	78.20	41.94	49.22	96.42
Wheat 3	97.70	95.45	94.32	94.15	96.70	85.69	79.52	85.02	95.06
Lucerne	80.60	93.84	94.72	96.88	96.91	93.52	92.13	93.83	95.34
Barley	75.98	93.51	94.38	88.86	94.79	93.04	97.11	97.76	95.98
Wheat	92.01	90.18	85.14	92.08	93.55	91.23	94.76	96.12	91.57
Grasses	42.66	56.55	77.20	80.94	93.36	92.39	96.60	98.64	86.41
Forest	94.08	89.52	93.57	90.49	93.95	93.48	87.56	88.49	91.13
Water	91.77	83.64	99.30	35.70	99.61	86.89	90.37	92.20	98.02
Building	0.00	8.57	37.69	81.77	87.48	85.39	59.74	72.99	84.09
OA	70.86	76.19	83.78	87.58	94.26	89.97	83.10	86.46	92.01
Kappa	0.681 7	0.740 4	0.823 1	0.865 1	0.937 8	0.885 7	0.818 2	0.854 0	0.912 7

种类	方法
种类	DBN^[37]	SAE^[44]	CNN^[63]	3D- CNN^[80]	FCN- SPUO^[82]	MI-SSL^[86]	KGraph- CNN^[88]	CNNSP^[89]	CV-3D- CNN^[95]	PCN^[98]
Stem beans	79.05	84.22	83.81	97.52	99.98	99.95	99.07	98.58	98.63	92.11
Rapeseed	39.33	74.19	67.52	90.03	98.87	99.64	99.15	94.44	97.48	95.76
Bare soil	0.00	93.78	60.72	97.67	99.91	99.43	100.00	99.67	92.74	98.71
Potatoes	93.74	81.55	90.75	90.78	99.44	99.97	99.63	99.73	93.60	96.49
Beet	93.25	88.96	95.25	91.47	98.93	99.95	99.76	97.11	95.21	91.07
Wheat 2	89.52	64.88	86.34	86.08	98.07	99.75	85.86	99.32	95.73	96.27
Peas	93.77	92.85	98.46	96.45	99.03	99.86	99.96	99.55	87.65	97.52
Wheat 3	97.70	94.32	97.01	97.46	99.78	99.95	99.01	98.21	99.44	96.87
Lucerne	80.60	94.72	80.54	95.31	97.45	99.98	98.10	92.33	84.81	96.13
Barley	75.98	94.38	85.62	97.46	99.17	99.60	98.08	97.29	84.14	92.23
Wheat	92.01	85.14	91.36	92.92	99.56	99.82	99.71	96.87	98.79	96.62
Grasses	42.66	77.20	64.86	91.54	99.68	99.51	92.50	93.41	72.39	96.32
Forest	94.08	93.57	87.49	96.03	99.89	99.98	98.81	99.19	99.85	99.05
Water	91.77	99.30	91.82	93.66	99.50	100.00	98.69	99.07	99.95	98.06
Building	0.00	37.69	85.44	73.61	90.51	96.95	76.84	95.78	96.22	81.26
OA	70.86	83.78	84.47	92.53	99.24	99.84	96.34	97.37	93.42	96.36
Kappa	0.681 7	0.823 1	0.830 7	0.918 6	0.991 8	0.995 9	0.960 2	0.971 4	0.928 5	0.960 4

极化SAR图像分类方法	代表性算法	优点	不足
基于DBN的极化SAR 图像分类方法	WRBM^[40], WBRBM^[41]	图像细节信息清晰,边缘保持得好;既包含了信念网络的优点,也保留了极化SAR的数据特性	没有考虑图像的空间信息;斑点噪声较明显;增加了网络的复杂度,训练时间成本增加
基于SAE的极化SAR 图像分类方法	SSAE-SL^[48], SAE-BWMRF^[45]	考虑极化SAR图像像素之间的空间信息	没有考虑极化SAR数据的统计分布信息和物理散射机制
	WAE^[54], MAE^[56]	图像细节信息清晰,边缘保持得好;既继承自编码网络的优点,也保留了极化SAR的数据特性	没有考虑图像的空间信息;斑点噪声较明显;增加了网络的复杂度,训练时间成本增加
	基于CNN的极化SAR 图像分类方法	3D-CNN^[80], CV-CNN^[91], CV-3D-CNN^[95],	图像区域一致性好;考虑了空间信息、特征之间的关系以及极化SAR数据的复数特性	图像细节信息不清晰;增加了网络的复杂度,训练时间成本增加
CV-FCN^[96], PCLNet^[87], KGraph-CNN^[88], CNNSP^[89]		图像区域一致性好;解决小样本问题	图像细节信息不清晰;增加了网络的复杂度,训练时间成本增加

Review on polarimetric SAR terrain classification methods using deep learning

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