多时相双极化SAR影像林地类型分类方法

doi:10.11707/j.1001-7488.20140312

林业科学 ›› 2014, Vol. 50 ›› Issue (3): 83-91.doi: 10.11707/j.1001-7488.20140312

多时相双极化SAR影像林地类型分类方法

王馨爽^1,2, 陈尔学¹, 李增元¹, 姚顽强³, 赵磊¹

1. 中国林业科学研究院资源信息研究所北京 100091;
2. 国家测绘地理信息局陕西基础地理信息中心西安 710054;
3. 西安科技大学测绘科学与技术学院西安 710054

收稿日期:2013-05-02 修回日期:2013-11-28 出版日期:2014-03-25 发布日期:2014-04-16
基金资助:
863计划课题“高分辨率SAR遥感综合实验与应用示范”（2011AA120405）；“面向对象SAR影像地物高可信解译技术”（2011AA120404）。

Multi-Temporal and Dual-Polarization SAR for Forest Land Type Classification

Wang Xinshuang^1,2, Chen Erxue¹, Li Zengyuan¹, Yao Wanqiang³, Zhao Lei¹

1. Research Institute of Forest Resources Information Techniques, CAF Beijing 100091;
2. Shaanxi Geomatics Center, National Administration of Surveying, Mapping and Geo-Information Xi'an 710054;
3. Department of Geomatics, Xi'an University of Science and Technology Xi'an 710054

Received:2013-05-02 Revised:2013-11-28 Online:2014-03-25 Published:2014-04-16
Contact: 陈尔学

摘要/Abstract

摘要：

森林在全球碳循环及大自然空气调节中发挥着重要作用，对森林分布的监测与制图意义重大。以黑龙江省逊克县两景星载ALOS PALSAR多时相数据覆盖区为研究区，利用不同时相极化SAR、干涉SAR对植被结构变化特征的敏感性，结合后向散射系数与干涉相干性的时变特征进行林地类型分类研究，发展基于多时相、多极化、干涉SAR的SVM林地类型分类识别方法。结果表明：多时相的平均干涉相干性对有林地、疏林地及灌木林地的识别十分有效；综合运用所选取的多时相干涉、极化比等有效维度信息能很好地突出地物的边缘与结构，更细致地区分不同林地类型。

关键词: 多时相, 极化, InSAR, 林地类型, 分类

Abstract:

Forest plays an important role on global carbon cycle and nature disturbance, so it is of great significance to monitor and map forest resources. Xunke County of Heilongjiang Province was selected as the test site and the coverage of two scenes of ALOS PALSAR images were applied. The sensitivity of multi-temporal PolSAR, InSAR with forest structure variation and time varying characteristics of backscattering coefficients and interferometric coherences were applied for forest land type classification. We developed a forest land type classification method based on SVM using multi-temporal, dual-polarization and interferometric SAR (InSAR) data. The result showed that the average InSAR coherence of multi-temporal could effectively identify forest land, sparse forest land and shrub land. The multi-temporal InSAR coherence, polarization ratios and other effective dimension information selected could effectively highlight the features and structures of objects and classify forest land types in detail.

Key words: multi-temporal, polarization, InSAR, forest land type, classification

中图分类号:

S757

王馨爽, 陈尔学, 李增元, 姚顽强, 赵磊. 多时相双极化SAR影像林地类型分类方法[J]. 林业科学, 2014, 50(3): 83-91.

Wang Xinshuang, Chen Erxue, Li Zengyuan, Yao Wanqiang, Zhao Lei. Multi-Temporal and Dual-Polarization SAR for Forest Land Type Classification[J]. Scientia Silvae Sinicae, 2014, 50(3): 83-91.

参考文献

白黎娜,李增元,陈尔学,等. 2003. 干涉测量土地利用影像分类决策树法森林识别研究. 林业科学,39 (1) :86-90.
(Bai L N, Li Z Y, Chen E X, et al. 2003. A study on forest identification with the decision tree for interferometric land-use image. Scientia Silvae Sinicae, 39 (1) :86-90. [in Chinese] )
陈尔学,李增元,车学俭,等. 1999. 星载SAR干涉测量数据用于森林识别的初步研究. 北京: 第五届全国计算机应用联合学术会议.
付仲良,张文元,孟庆祥. 2012. 灰度和纹理特征组合的SAR影像SVM分类. 应用科学学报, 30 (5) :498-504.
(Fu Z L, Zhang W Y, Meng Q X. 2012. SAR image classification based on SVM with fusion of gray scale and texture features. Journal of Applied Sciences, 30 (5) :498-504. [in Chinese] )
郎丰铠,杨杰,赵伶,等. 2012. 基于Freeman散射熵和各向异性度的极化SAR影像分类算法研究. 测绘学报,41 (4) :556-562.
(Lang F K, Yang J, Zhao L, et al. 2012. Polarimetric sar data classification with Freeman entropy and anisotropy analysis. Acta Geodaetica et Cartographica Sinica, 41 (4) :556-562. [in Chinese] )
汤井田,胡丹,龚智敏. 2008. 基于SVM的极化SAR图像分类研究. 遥感技术与应用,23 (3) :341-344.
(Tang J T, Hu D, Gong Z M. 2008. Study of classification by support vector machine on synthetic aperture radar image. Remote Sensing Technology and Application, 23 (3) :341-344. [in Chinese] )
赵英时. 2003. 遥感应用分析原理与方法. 北京: 科学出版社.
Bamler R, Hartl P. 1998. Synthetic aperture radar interferometry. Inverse Problems, 14 (4) :1-54.
Chen Q, Kuang G, Li J, et al. 2013. Unsupervised land cover/land use classification using PolSAR imagery based on scattering similarity. IEEE Transactions on Geoscience and Remote Sensing, 51 (3) :1817-1825.
Kilpi J, Ahola H A, Rauste Y, et al. 2013. Improved mapping of tropical forests with optical and SAR imagery, Part I: forest cover and accuracy assessment using multi-resolution data. Earth Observations and Remote Sensing, 6 (1) :74-91.
Krylov V A, Moser G, Serpico S B, et al. 2011. Supervised high-resolution dual-polarization SAR image classification by finite mixtures and copulas. Selected Topics in Signal Processing, IEEE Journal of, 5 (3) :554-566.
Lee J S, Grunes M R, Ainsworth T, et al. 2005. Forest classification based on L-band polarimetric and interferometric SAR data. Proceedings of the 2nd International Workshop POLINSAR 2005 (ESA SP-586),6.1-6.7.
Liesenberg V, Gloaguen R. 2013. Evaluating SAR polarization modes at L-band for forest classification purposes in Eastern Amazon, Brazil. International Journal of Applied Earth Observation and Geoinformation, 21:122-135.
Liu M, Zhang H, Wang C. 2011. Applying the log-cumulants of texture parameter to fully polarimetric SAR classification using support vector machines classifier. Radar (Radar). 2011 IEEE CIE International Conference on, 728-731.
Luo H M, Chen E X, Li X W, et al. 2010. Unsupervised classification of forest from polarimetric interferometric SAR data using fuzzy clustering. Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, 201-206.
Oliver C, Quegan S. 1998. Understanding synthetic aperture radar images. Norwood: Artech House Inc, 219-222.
Strozzi T, Dammert P B G, Wegmuller U, et al. 2000. Landuse mapping with ERS SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(2) :766-775.
Ulaby F T, Moore R K, Fung A K. 1981. Microwave remote sensing: active and passive, vol.2, radar remote sensing and surface scattering and emission theory.Addison-Wesley, Advanced Book Program, Reading, Massachusetts, 273-284.
Wang S, Liu K, Pei J, et al. 2013. Unsupervised classification of fully polarimetric SAR images based on scattering power entropy and copolarized ratio. Geoscience and Remote Sensing, 10(3):622-626.

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多时相双极化SAR影像林地类型分类方法

Multi-Temporal and Dual-Polarization SAR for Forest Land Type Classification

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