Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (4) : 48-56     DOI: 10.6046/gtzyyg.2017.04.09
Research on methods of building area extraction from high resolution SAR image based on manifold learning
CUI Shiai1,2, CHENG Bo1, LIU Yueming1,2
1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
2. University of the Chinese Academy of Sciences, Beijing 100094, China
Download: PDF(12204 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  The characteristics of high resolution SAR image is nonlinear and of high dimension. The description of SAR image in which a low dimensional manifold is embedded in high dimensional space is more useful for targets recognition. Therefore, a novel scheme of high resolution SAR image building area extraction is proposed by applying manifold learning to feature representation of a high dimensional SAR targets recognition. Firstly, the high resolution SAR image was preprocessed, and then eight texture features were extracted with gray level co-occurrence matrix (GLCM)so as to construct feature set with gray feature. Adaptive neighborhood selection neighborhood preserving embedding (ANSNPE)algorithm was used to extract the new features from the feature set. Finally, the building area was extracted by threshold segmentation with the new features and post processing, and the accuracy was evaluated. Selecting TerraSAR-X as test data, the authors carried out the experiments. The results show that ANSNPE algorithm can effectively extract the building area from high resolution SAR image, and has strong generalization capability. The projection matrix obtained through the training data can be directly applied to the new samples, and the accuracy of building area extraction could reach higher than 85%.
Keywords passive microwave remote sensing      land surface parameter      radio-frequency interference (RFI)      eastern Asia land     
:  TP751.1  
Issue Date: 04 December 2017
E-mail this article
E-mail Alert
Articles by authors
WU Ying
WANG Zhenhui
Cite this article:   
WU Ying,QIAN Bo,WANG Zhenhui. Research on methods of building area extraction from high resolution SAR image based on manifold learning[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 48-56.
URL:     OR
[1] 邵 芸,范湘涛,刘 浩.基于目标时域散射特性的土地覆盖类型分类研究[J].国土资源遥感,2001,13(4):40-49,67.doi:10.6046/gtzyyg.2001.04.07.
Shao Y,Fan X T,Liu H.Land cover classification based on temporal backscatter signatures of the targets[J].Remote Sensing for Land and Resources,2001,13(4):40-49,67.doi:10.6046/gtzyyg.2001.04.07.
[2] 谭衢霖,邵 芸.雷达遥感图像分类新技术发展研究[J].国土资源遥感,2001,13(3):1-7.doi:10.6046/gtzyyg.2001.03.01.
Tan Q L,Shao Y.A study on the development of new classification technology for redar remote sensing imagery[J].Remote Sensing for Land and Resources,2001,13(3):1-7.doi:10.6046/gtzyyg.2001.03.01.
[3] 赵凌君,高 贵,匡纲要.基于变差函数纹理特征的高分辨率SAR图像建筑区提取[J].信号处理,2009,25(9):1433-1442.
Zhao L J,Gao G,Kuang G Y.Variogram-based built-up areas extraction from high-resolution SAR images[J].Signal Processing,2009,25(9):1433-1442.
[4] 朱俊杰,郭华东,范湘涛,等.单波段单极化高分辨率SAR图像纹理分类研究[J].国土资源遥感,2005,17(2):36-39.doi:10.6046/gtzyyg.2005.02.09.
Zhu J J,Guo H D,Fan X T,et al.The application of the wavelet texture method to the classification of single-band,single-polarized and high-resolution SAR images[J].Remote Sensing for Land and Resources,2005,17(2):36-39.doi:10.6046/gtzyyg.2005.02.09.
[5] 徐 佳,陈媛媛,黄其欢,等.综合灰度与纹理特征的高分辨率星载SAR图像建筑区提取方法研究[J].遥感技术与应用,2012,27(5):692-698.
Xu J,Chen Y Y,Huang Q H,et al.Built-up areas extraction in high resolution spaceborne SAR image based on the integration of grey and texture features[J].Remote Sensing Technology and Application,2012,27(5):692-698.
[6] 赵凌君,秦玉亮,高 贵,等.利用GLCM纹理分析的高分辨率SAR图像建筑区检测[J].遥感学报,2009,13(3):483-490.
Zhao L J,Qin Y L,Gao G,et al.Detection of built-up areas from high-resolution SAR images using the GLCM textural analysis[J].Journal of Remote Sensing,2009,13(3):483-490.
[7] Tu S T,Chen J Y,Yang W,et al.Laplacian eigenmaps-based polarimetric dimensionality reduction for SAR image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(1):170-179.
[8] 黄启宏,刘 钊.流形学习中非线性维数约简方法概述[J].计算机应用研究,2007,24(11):19-25.
Huang Q H,Liu Z.Overview of nonlinear dimensionality reduction methods in manifold learning[J].Application Research of Computers,2007,24(11):19-25.
[9] Tenenbaum J B,de Silva V,Langford J C.A global geometric
framework for nonlinear dimensionality reduction[J].Science,2000,290(5500):2319-2323.
[10] Roweis S T,Saul L K.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.
[11] Belkin M,Niyogi P.Laplacian eigenmaps for dimensionality reduction and data representation[J].Neural Computation,2003,15(6):1373-1396.
[12] Zhang Z Y,Zha H Y.Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J].SIAM Journal on Scientific Computing,2005,26(1):313-338.
[13] He X F,Niyogi P.Locality Preserving Projections[C]//Advances in neural information processing systems (NIPS).Cambridge:MIT Press,2003,16:153-160.
[14] He X F,Cai D,Yan S C,et al.Neighborhood preserving embedding[C]//Proceedings of the 10th IEEE international conference on computer vision.Beijing,China:2005,2:1208-1213.
[15] 李 婷.基于流形学习的高分辨率SAR图像城市建筑区识别方法研究[D].北京:中国科学院大学,2015.
Li T.Method Research of Recognition of Urban Building Areas from High Resolution SAR Images Based on Manifold Learning[D].Beijing:University of Chinese Academy of Sciences,2015.
[16] 刘花丽.基于流形学习算法的人脸识别研究[D].兰州:兰州理工大学,2013.
Liu H L.Research on Face Recognition Based on Manifold Learning Algorithm[D].Lanzhou:Lanzhou University of Technology,2013.
[17] 左加阔.基于流形学习算法的新生儿疼痛表情识别[D].南京:南京邮电大学,2011.
Zuo J K.Manifold Learning Algorithm for Facial Expression Recognition of Pain in Neonates[D].Nanjing:Nanjing University of Posts and Telecommunications,2011.
[18] 惠康华,肖柏华,王春恒.基于自适应近邻参数的局部线性嵌入[J].模式识别与人工智能,2010,23(6):842-846.
Hui K H,Xiao B H,Wang C H.Self-regulation of neighborhood parameter for locally linear embedding[J].Pattern Recognition and Artificial Intelligence,2010,23(6):842-846.
[19] Huang L Z,Zheng L X,Chen C Y,et al.Locally linear embedding algorithm with adaptive neighbors[C]//Proceedings of international workshop on intelligent systems and applications.Wuhan,China:IEEE,2009:1-4.
[20] 张育林,庄 健,王 娜,等.一种自适应局部线性嵌入与谱聚类融合的故障诊断方法[J].西安交通大学学报,2010,44(1):77-82.
Zhang Y L,Zhuang J,Wang N,et al.Fusion of adaptive local linear embedding and spectral clustering algorithm with application to fault diagnosis[J].Journal of Xi’an Jiaotong University,2010,44(1):77-82.
[21] Haralick R M,Shanmugam K,Dinstein I H.Textural features for image classification[J].IEEE Transactions on Systems,Man,and Cybernetics,1973,SMC-3(6):610-621.
[22] 吴 樊,王 超,张 红.基于纹理特征的高分辨率SAR影像居民区提取[J].遥感技术与应用,2005,20(1):148-152.
Wu F,Wang C,Zhang H.Residential areas extraction in high resolution SAR image based on texture features[J].Remote Sensing Technology and Application,2005,20(1):148-152.
[1] Ying WU, Sulin JIANG, Zhenhui WANG. Effect of radio-frequency interference on the retrieval of land surface temperature from microwave radiation imager[J]. Remote Sensing for Land & Resources, 2018, 30(4): 90-96.
[2] Ying WU, Sulin JIANG, Zhenhui WANG. Comparison and analysis of the interference identification methods for microwave measurements over snow land[J]. Remote Sensing for Land & Resources, 2018, 30(3): 40-47.
[3] WU Ying, QIAN Bo, WANG Zhenhui. Effect of radio-frequency interference on the land surface parameters retrieval from passive microwave remote sensing data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 176-181.
[4] BAI Shuying, WU Qi, SHI Jianqiao, GU Haimin. Relationship between the spatial and temporal distribution of snow depth and the terrain over the Tibetan Plateau[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 171-178.
[5] SUN Zhiwen, YU Pengshan, XIA Lang, WU Shengli, JIANG Lingmei, GUO Lei. Progress in study of snow parameter inversion by passive microwave remote sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 9-15.
[6] ZHOU Fangcheng, SONG Xiaoning, LI Zhaoliang. Progress of land surface temperature retrieval based on passive microwave remote sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(1): 1-7.
[7] WU Ying, WANG Zhen-hui. Advances in the Study of Land Surface Emissivity Retrieval from Passive Microwave Remote Sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 1-7.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech