极化SAR参数优化与光学波谱相结合的面向对象土地覆盖分类

doi:10.11947/j.AGCS.2019.20170746

摘要/Abstract

摘要： 提出一种基于极化参数优化的面向对象分类方法。该方法结合光学和SAR数据，有效提高了对地物的识别能力。本文方法的关键在于：在H-A-α分解中，使用光学影像指导SAR影像选择同质点，使其更精确地估计极化参数并结合光学波谱信息作为输入特征；使用面向对象的分类方法，仅将光学影像作为分割输入，避免SAR噪声引起的分割错误。以美国Bakersfield地区的Sentinel-1/2数据为例，确定7种地物类型，对比分析不同输入与不同分类器对分类结果的影响。研究表明，优化输入参数在纹理丰富区域能够有效提高分类精度；面向对象的分类结果更加稳定并较好地维持地表几何特征；改进分类方法较传统分类方法总体精度提高了近10%，达到92.6%。

关键词: 合成孔径雷达, 极化, 多光谱, 数据融合, 面向对象, 土地覆盖分类

Abstract: An object-based approach is proposed for land cover classification using optimal polarimetric parameters. The ability to identify targets is effectively enhanced by the integration of SAR and optical images. The innovation of presented method can be summarized in the following two main points:① estimating polarimetric parameters (H-A-α decomposition) through optical image as a driver; ② a multi-resolution segmentation based on optical image only is deployed to refine classification results. The proposed method is verified by using Sentinel-1/2 datasets over Bakersfield area, California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database (NLCD). A detailed accuracy assessment complied for seven classes of surfaces shows that the proposed method outperforms the conventional approach by around 10%, with an overall accuracy of 92.6% over regions with rich texture.

Key words: synthetic aperture radar(SAR), polarimetric, multispectral, data fusion, object-based, land-cover classification

中图分类号:

P237

赵诣, 蒋弥. 极化SAR参数优化与光学波谱相结合的面向对象土地覆盖分类[J]. 测绘学报, 2019, 48(5): 609-617.

ZHAO Yi, JIANG Mi. Integration of SAR polarimetric parameters and multi-spectral data for object-based land cover classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(5): 609-617.

参考文献

[1] CONGALTON R G, GU Jianyu, YADAV K, et al. Global land cover mapping:a review and uncertainty analysis[J]. Remote Sensing, 2014, 6(12):12070-12093.
[2] DI VITTORIO C A, GEORGAKAKOS A P. Land cover classification and wetland inundation mapping using MODIS[J]. Remote Sensing of Environment, 2018(204):1-17.
[3] ROSSI C, ERTEN E. Paddy-rice monitoring using tandem-X[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2):900-910.
[4] 陈军, 陈利军, 李然, 等. 基于Globeland30的全球城乡建设用地空间分布与变化统计分析[J]. 测绘学报, 2015, 44(11):1181-1188. DOI:10.11947/j.AGCS.2015.20140677. CHEN Jun, CHEN Lijun, LI Ran, et al. Spatial distribution and ten years change of global built-up areas derived from GlobeLand30[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(11):1181-1188. DOI:10.11947/j.AGCS.2015.20140677.
[5] SABINS F F. Remote sensing:principles and applications[M]. 3rd ed. Long Grove:Waveland Press, 2007.
[6] ZHANG H K, ROY D P. Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m landsat land cover classification[J]. Remote Sensing of Environment, 2017(197):15-34.
[7] OLIVER C, QUEGAN S. Understanding synthetic aperture radar images[M]. Raleigh:SciTech Publishing, 2004.
[8] DOBSON M C, PIERCE L E, ULABY F T. Knowledge-based land-cover classification using ERS-1/JERS-1 SAR composites[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(1):83-99.
[9] GENG Jie, FAN Jianchao, WANG Hongyu, et al. High-resolution SAR image classification via deep convolutional autoencoders[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(11):2351-2355.
[10] DU Peijun, SAMAT A, WASKE B, et al. Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015(105):38-53.
[11] UHLMANN S, KIRANYAZ S. Integrating color features in polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4):2197-2216.
[12] HUANG Xiaodong, WANG Jinfei, SHANG Jiali, et al. Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric Radarsat-2 imagery[J]. Remote Sensing of Environment, 2017(193):11-28.
[13] CHEN Siwei, TAO Chensong. Multi-temporal PolSAR crops classification using polarimetric-feature-driven deep convolutional neural network[C]//Proceedings of 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP). Shanghai, China:IEEE, 2017:1-4.
[14] 王馨爽, 陈尔学, 李增元, 等. 多时相双极化合成孔径雷达干涉测量土地覆盖分类方法[J]. 测绘学报, 2015, 44(5):533-540. DOI:10.11947/j.AGCS.2015.20130244. WANG Xinshuang, CHEN Erxue, LI Zengyuan, et al. Multi-temporal and dual-polarization interferometric SAR for land cover type classification[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(5):533-540. DOI:10.11947/j.AGCS.2015.20130244.
[15] BRISCO B, BROWN R J. Multidate SAR/TM synergism for crop classification in western Canada[J]. Photogrammetric Engineering & Remote Sensing, 1992, 61(8):1009-1014.
[16] ABDIKAN S, BILGIN G, SANLI F B, et al. Enhancing land use classification with fusing dual-polarized TerraSAR-X and multispectral rapid eye data[J]. Journal of Applied Remote Sensing, 2015, 9(1):096054.
[17] JOSHI N, BAUMANN M, EHAMMER A, et al. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring[J]. Remote Sensing, 2016, 8(1):70.
[18] ZHU Zhe, WOODCOCK C E, ROGAN J, et al. Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data[J]. Remote Sensing of Environment, 2012(117):72-82.
[19] LAVRENIUK M, KUSSUL N, MERETSKY M, et al. Impact of SAR data filtering on crop classification accuracy[C]//Proceedings of 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). Kiev:IEEE, 2017:912-917.
[20] JIANG Mi, YONG Bin, TIAN Xin, et al. The potential of more accurate InSAR covariance matrix estimation for land cover mapping[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017(126):120-128.
[21] QIN Fachao, GUO Jiming, LANG Fengkai. Superpixel segmentation for polarimetric SAR imagery using local iterative clustering[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(1):13-17.
[22] RODRIGUES F A Á, NETO J F S R, MARQUES R C P, et al. SAR image segmentation using the roughness information[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2):132-136.
[23] TORRES R, SNOEIJ P, GEUDTNER D, et al. GMESSentinel-1 mission[J]. Remote Sensing of Environment, 2012(120):9-24.
[24] ABDIKAN S, SANLI F B, USTUNER M, et al. Land cover mapping using Sentinel-1 SAR data[C]//ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague:ISPRS, 2016(XLI-B7):757-761.
[25] ROYCHOWDHURY K. Comparison between spectral, spatial and polarimetric classification of urban and periurban landcover using temporal Sentinel-1 images[C]//ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague:ISPRS, 2016(XLI-B7):789-796.
[26] SUSAKI J, KAJIMOTO M, KISHIMOTO M. Urban density mapping of global megacities from polarimetric SAR images[J]. Remote Sensing of Environment, 2014(155):334-348.
[27] GENG Xiaomeng, LI Xiaoming, VELOTTO D, et al. Study of the polarimetric characteristics of mud flats in an intertidal zone using C-and X-band spaceborne SAR data[J]. Remote Sensing of Environment, 2016(176):56-68.
[28] WANG Hongquan, MAGAGI R, GOITA K. Comparison of different polarimetric decompositions for soil moisture retrieval over vegetation covered agricultural area[J]. Remote Sensing of Environment, 2017(199):120-136.
[29] HUYNEN J R. Phenomenological theory of radar targets[M]//USLENGHI P L E. Electromagnetic Scattering. New York:Academic Press, 1978.
[30] CLOUDE S. The dual polarization entropy/alpha decomposition:a PALSAR case study[C]//LACOSTE H, OUWEHAND L. Proceedings of the the 3rd International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry.Noordwijk:European Space Agency, 2007.
[31] CLOUDE S R, POTTIER E. An entropy based classification scheme for land applications of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1):68-78.
[32] JIANG Mi, DING Xiaoli, HANSSEN R F, et al. Fast statistically homogeneous pixel selection for covariance matrix estimation for multitemporal InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3):1213-1224.
[33] VERDOLIVA L, GAETANO R, RUELLO G, et al. Optical-driven nonlocal SAR despeckling[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(2):314-318.
[34] RIDLER T W, CALVARD S. Picture thresholding using an iterative selection method[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1978, 8(8):630-632.
[35] GUERBAI Y, CHIBANI Y, HADJADJI B. The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters[J]. Pattern Recognition, 2015, 48(1):103-113.
[36] ZHENG Xiulian, YE Hong, TANG Yinggan. Image bi-level thresholding based on gray level-local variance histogram[J]. Entropy, 2017, 19(5):191.
[37] 杜培军, 夏俊士, 薛朝辉, 等. 高光谱遥感影像分类研究进展[J]. 遥感学报, 2016, 20(2):236-256. DU Peijun, XIA Junshi, XUE Zhaohui, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016, 20(2):236-256.
[38] WEIH R C JR, RIGGAN N D JR. Object-based classification vs. pixel-based classification:comparitive importance of multi-resolution imagery[C]//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague:ISPRS, 2010.
[39] 冯文卿, 眭海刚, 涂继辉, 等. 联合像素级和对象级分析的遥感影像变化检测[J]. 测绘学报, 2017, 46(9):1147-1155, 1164. DOI:10.11947/j.AGCS.2017.20160606. FENG Wenqing, SUI Haigang, TU Jihui, et al. Remote sensing image change detection based on the combination of pixel-level and object-level analysis[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(9):1147-1155, 1164. DOI:10.11947/j.AGCS.2017.20160606.
[40] BENZ U C, HOFMANN P, WILLHAUCK G, et al. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004, 58(3-4):239-258.
[41] HOMER C, DEWITZ J, YANG Limim, et al. Completion of the 2011 national land cover database for the conterminous united states-representing a decade of land cover change information[J]. Photogrammetric Engineering & Remote Sensing, 2015, 81(5):345-354.
[42] VASILE G, TROUVÉ E, LEE J S, et al. Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6):1609-1621.
[43] 田馨, 廖明生. 精确提取InSAR时间去相关分量的方法[J]. 红外与毫米波学报, 2016, 35(4):454-461. TIAN Xin, LIAO Mingsheng. Accurate extraction of InSAR temporal decorrelation component[J]. Journal of Infrared and Millimeter Waves, 2016, 35(4):454-461.
[44] VAPNIK V N. An overview of statistical learning theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5):988-999.
[45] MOUNTRAKIS G, IM J, OGOLE C. Support vector machines in remote sensing:a review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(3):247-259.