综合ULBP与Gabor纹理特征的SAR影像建筑区提取方法研究

邵乐; 王晓青; 翟玮; 丁香

doi:10.11899/zzfy20230120

综合ULBP与Gabor纹理特征的SAR影像建筑区提取方法研究

doi: 10.11899/zzfy20230120

邵乐^1,,
王晓青^1, ,,
翟玮²,
丁香¹

1.
中国地震局地震预测研究所, 北京 100036
2.
甘肃省地震局, 兰州 730000

基金项目: 高分辨对地观测重大专项（民用部分）科研项目（31-Y30F09-9001-20/22-12）

详细信息

作者简介:
邵乐，女，生于1997年。硕士研究生。主要从事SAR图像震害监测评估研究。E-mail：shaole0904@163.com

通讯作者:
王晓青，男，生于1963年。研究员。主要从事地震灾害风险评估与遥感应用研究。E-mail：wangxiaoq517@163.com

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出版历程
- 收稿日期: 2022-07-13
- 刊出日期: 2023-03-31

Research on Building Area Extraction Method of SAR Image Integrating ULBP and Gabor Texture Features

1.
Institute of Earthquake Science, China Earthquake Administration, Beijing 100036, China
2.
Earthquake Administration of Gansu Province, Lanzhou 730000, China

摘要

摘要: 建筑区提取对于地震灾情快速评估和地震灾害风险识别至关重要，合成孔径雷达（SAR）影像的纹理特征研究可在建筑区提取方面发挥重要作用。利用全极化SAR影像，提出综合使用ULBP纹理特征和Gabor纹理特征的建筑区提取方法。在SAR数据预处理的基础上，首先对基于Gabor滤波的纹理特征影像进行主成分分析，保留前2个最优主成分纹理特征影像；然后与ULBP纹理特征进行波段组合；最后利用支持向量机监督分类方法对组合后的影像进行分类，获得建筑区。研究结果表明，综合使用ULBP纹理特征和Gabor纹理特征可得到更高的建筑区提取精度，总体分类精度达90%，Kappa系数为0.78。
- SAR影像 /
- ULBP纹理特征 /
- Gabor纹理特征 /
- 建筑区提取
Abstract: Building area extraction is very important for the rapid assessment of earthquake disasters and the identification of earthquake disaster risks. Research on texture features of synthetic aperture radar ( SAR ) images can play an important role in building area extraction. Using fully polarimetric SAR images, a building area extraction method using ULBP texture features and Gabor texture features is proposed. On the basis of SAR data preprocessing, the texture feature image based on the Gabor filter is analyzed by principal component analysis, the first two optimal principal component texture feature images are retained. Then, band combination is performed with ULBP texture features. Finally, the support vector machine supervised classification method is used to classify the combined image to obtain the building area. The results show that the comprehensive use of the ULBP texture feature and Gabor texture features can obtain higher building area extraction accuracy, the overall classification accuracy is 90 %, and the Kappa coefficient is 0.78.
- SAR image /
- ULBP texture feature /
- Gabor texture feature /
- Building area extraction

HTML全文

图 1 建筑区主要散射机制（刘康，2012）

Figure 1. Main scattering mechanisms in built-up areas

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图 2 LBP算子

Figure 2. LBP operator

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图 3 建筑区提取流程

Figure 3. Technical process of building area extraction

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图 4 研究区影像

Figure 4. Study area image

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图 5 ULBP纹理特征

Figure 5. ULBP texture features

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图 6 纹理特征影像的建筑区与非建筑区均值-方差分布

Figure 6. The mean-variance distribution diagram of texture feature image in building area and no-building area

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图 7 Gabor纹理特征

Figure 7. Gabor texture features

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图 8 综合LBP、Gabor纹理特征主成分的特征组合影像

Figure 8. Feature combination image combining LBP and Gabor texture feature principal components

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图 9 综合LBP、Gabor纹理特征主成分的建筑区提取结果

Figure 9. Extraction results of building area based on LBP and Gabor texture features principal components

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图 10 单独使用ULBP、Gabor纹理特征的建筑区提取结果

Figure 10. Extraction results of building area using LBP and Gabor texture features alone

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表 1 Gabor纹理特征主成分分析的特征值和累计贡献率

Table 1. Eigenvalue and cumulative contribution rate of Gabor texture feature principal component analysis

主分量序号	特征值	累计贡献率/%
1	169 895.662 9	78.57
2	35 276.230 6	94.89
3	4 141.754 3	96.80
4	3 023.798 2	98.20
5	2 138.256 2	99.19
6	1 011.151 3	99.66
7	527.956 9	99.90
8	180.849 1	100.00

下载: 导出CSV

表 2 试验区混淆矩阵及精度评价

Table 2. Confusion matrix and accuracy evaluation of experimental area

方法	类别	非建筑区/ m²	建筑区/ m²	总数/ m²	错分率/%	误分率/%	生产者精度 /%	用户精度/%	Kappa系数
综合使用ULBP、Gabor纹理特征	非建筑区	258 436	25 560	283 996	8	6	94	91	0.78
	建筑区	17 933	120 017	137 950	13	18	82	87
	总体精度/%		90
单独使用ULBP纹理特征	非建筑区	238 958	75 461	314 419	24	5	95	76	0.57
	建筑区	11 233	113 576	124 809	9	40	60	91
	总体精度/%		80
单独使用Gabor纹理特征	非建筑区	259 773	53 207	312 980	17	4	96	83	0.64
	建筑区	13 295	112 894	126 189	11	32	68	89
	总体精度/%		84

下载: 导出CSV

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