基于空间与光谱注意力的光学图像和SAR图像特征融合分类方法

姜文; 潘洁; 朱金彪; 岳昔娟

doi:10.11999/JEIT220063

基于空间与光谱注意力的光学图像和SAR图像特征融合分类方法

doi: 10.11999/JEIT220063

姜文¹,
潘洁^1, ,,
朱金彪^{1, 2},
岳昔娟¹

1.
中国科学院空天信息创新研究院北京 100094
2.
西北工业大学西安 710072

详细信息

作者简介:
姜文：男，助理研究员，博士，研究方向为合成孔径雷达信号处理和应用

潘洁：女，正高级工程师，博士，研究方向为航空遥感技术与应用

朱金彪：男，正高级工程师，硕士，研究方向为航空遥感技术与应用

岳昔娟：女，高级工程师，博士，研究方向为合成孔径雷达信息处理和应用

通讯作者:
潘洁　panjie@aircas.ac.cn

中图分类号: TN958; TP751
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- 被引次数: 0
出版历程
- 收稿日期: 2022-01-13
- 修回日期: 2022-05-28
- 网络出版日期: 2022-06-10
- 刊出日期: 2023-03-10

Feature Fusion Classification for Optical Image and SAR Image Based on Spatial-spectral Attention

JIANG Wen¹,
PAN Jie^{1
, ,},
ZHU Jinbiao^{1, 2},
YUE Xijuan¹

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
Northwestern Polytechnical University, Xi’an 710072, China

摘要

摘要: 针对多源遥感图像的差异性和互补性问题，该文提出一种基于空间与光谱注意力的光学图像和SAR图像特征融合分类方法。首先利用卷积神经网络分别进行光学图像和SAR图像的特征提取，设计空间注意力和光谱注意力组成的注意力模块分析特征重要程度，生成不同特征的权重进行特征融合增强，同时减弱对无效信息的关注，从而提高光学和SAR图像融合分类精度。通过在两组光学和SAR图像数据集上进行对比实验，结果表明所提方法取得更高的融合分类精度。
- SAR图像 /
- 深度学习 /
- 特征融合 /
- 注意力机制
Abstract: Considering the issue of difference and complementarity of multi-source remote sensing images, this paper proposes a feature fusion classification method for optical image and SAR image based on spatial-spectral attention. Firstly, features of optical image and SAR image are extracted by the convolutional neural network, and an attention module composed of spatial attention and spectral attention is designed to analyze the importance of features. Features can be enhanced by the weights of the attention module, which can reduce the attention to irrelevant information, and thus improve the accuracy of fusion classification for optical and SAR images. Experimental results on two datasets of optical image and SAR image demonstrate that the proposed method is able to yield higher fusion classification accuracy.
- SAR image /
- Deep learning /
- Feature fusion /
- Attention mechanism

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图 1 空间与光谱注意力的特征融合模型

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图 2 注意力模块

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图 3 数据集A

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图 4 数据集B

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图 5 数据集A在各种方法下的结果对比图

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图 6 数据集B在各种方法下的结果对比图

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图 7 数据集D在各种方法下的结果对比图

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表 1 数据集各类别数目

类别	颜色	数据集A	数据集A训练集	数据集A验证集	数据集A测试集	数据集B	数据集B训练集	数据集B验证集	数据集B测试集
建筑		129629	20000	20000	89629	200251	20000	20000	160251
公路		305221	30000	20000	255221	320238	30000	20000	270238
裸土		284388	30000	20000	234388	539888	50000	40000	449888
草坪		695808	70000	60000	565808	263817	20000	20000	223817
树木		84954	10000	10000	64954	175615	20000	20000	135615
总计	–	1500000	160000	130000	1210000	1500000	140000	120000	1240000

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表 2 不同识别方法对应的5类数据集A识别正确率(%)及评价指标OA, AA, Kappa值

	CNN(S)	CNN-ATT(S)	CNN(O)	CNN-ATT(O)	CNN(S+O)	Two-branch	MDL-Middle	TAFFN	CNN-ATT(S+O)
建筑公路裸土草坪树木	72.1875.9153.5971.5147.37	75.9776.1154.4971.9243.38	96.9494.7684.1795.5870.50	96.2695.0886.4194.8970.81	95.6994.9586.4094.5980.03	95.5891.5683.1991.4960.90	97.2092.3584.9491.8071.35	94.9894.1787.8193.8478.57	94.9695.6891.9292.7685.91
OA	68.98	69.39	91.58	91.88	92.33	88.46	90.06	92.03	93.06
AA	64.11	64.37	88.39	88.69	90.22	84.55	87.53	89.87	92.25
Kappa	0.5349	0.5415	0.8805	0.8843	0.8897	83.47	85.68	0.8856	0.8997

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表 3 不同识别方法对应的5类数据集B识别正确率(%)及评价指标OA, AA, Kappa值

	CNN(S)	CNN-ATT(S)	CNN(O)	CNN-ATT(O)	CNN(S+O)	Two-branch	MDL-Middle	TAFFN	CNN-ATT(S+O)
建筑公路裸土草坪树木	72.7973.1156.8860.0747.58	74.3371.0857.5459.0149.56	97.0591.2288.8683.3377.66	97.9291.1988.2383.8479.86	96.2692.2487.5685.9579.86	93.7386.0381.7979.9971.91	94.7188.9484.4081.6976.42	96.4693.2385.9884.5281.64	96.4192.8288.6787.0184.17
OA	59.67	60.60	88.11	88.30	88.34	82.64	85.21	88.03	89.75
AA	51.74	51.92	73.02	73.42	73.64	68.91	71.03	73.63	74.84
Kappa	44.97	0.4626	0.8436	0.8458	0.8462	77.04	80.48	0.8416	0.8648

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表 4 不同识别方法对应的数据集A和B训练和测试时间(s)

数据集	时间	CNN(S)	CNN-ATT(S)	CNN(O)	CNN-ATT(O)	CNN(S+O)	Two-branch	MDL-Middle	TAFFN	CNN-ATT(S+O)
A	训练时间	339.794	492.978	368.404	505.159	534.780	282.893	365.131	440.552	1262.196
A	测试时间	43.584	56.762	45.952	58.979	58.454	89.591	65.505	129.132	119.097
B	训练时间	285.813	473.852	301.061	462.599	477.815	218.434	299.963	355.166	1060.421
B	测试时间	42.754	54.065	42.538	66.526	56.818	59.116	66.372	99.237	103.138

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表 5 交叉验证数据集介绍

类别	1	2	3	4	5	总计
训练集	10000	20000	30000	22000	9000	91000
测试集	116741	167431	327719	99719	38390	750000

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表 6 交叉验证识别正确率(%)及评价指标OA, AA, Kappa值

方法	CNN(S+O)	Two-branch	MDL-Middle	TAFFN	CNN-ATT(S+O)
建筑公路裸土草坪树木	91.5470.7987.6445.4949.75	87.9765.4186.9747.7050.21	89.9767.8385.9449.9251.57	92.8066.2987.4549.0057.27	92.4664.6887.4453.1758.04
OA	72.19	71.52	72.64	72.71	73.07
AA	69.04	67.65	69.05	70.56	71.16
Kappa	62.80	61.67	63.00	63.19	63.67

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