高分辨InSAR中的城市高层建筑特征提取

doi:10.19665/j.issn1001-2400.2019.04.019

Abstract

Abstract:

Under decimeter resolution, the projection of an individual high-rise building shows its distinctive features in interferometric synthetic aperture radar (InSAR) images, which can help extract more accurate detail information and supply prior information for advanced interferometric techniques such as Tomography SAR (TomoSAR). In this paper, a top-to-down coarse-to fine approach is proposed to extract high-rise building features, which works at four different levels, namely, image-, region of interest (ROI)-, building- and pixel-wise processing, and which considers the analysis of high-rise building characteristics in very high resolution InSAR data pairs. Finally, the features of the high-rise building including the building orientation and pixels at the same height with the similar orientation are defined and estimated. The proposed approach is validated by using the single-pass interferogram from the TanDEM-X system over high-rise urban areas.

Key words: very high resolution, interferometric synthetic aperture radar, high-rise building, feature extraction, pixels in the same height with the similar orientation

CLC Number:

TP959.3

GUO Rui,ZANG Bo,PENG Shuming,XING Mengdao. Extraction of features of the urban high-rise building from high resolution InSAR data[J].Journal of Xidian University, 2019, 46(4): 137-143.

Figures/Tables 9

References 11

[1]	UEMOTOJ, NADAI A, KOJIMA S , et al. Extraction and Height Estimation of Artificial Vertical Structures Based on the Wrapped Interferometric Phase Difference within Their Layovers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018,139:14-29.
[2]	GE N, GONZALEZ F R, WANG Y , et al. Spaceborne Staring Spotlight SAR Tomography—a First Demonstration with TerraSAR-X[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018,11(10):3743-3756.
[3]	AUER S, GISINGER C, TAO J . Characterization of Facade Regularities in High-resolution SAR Image[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(5):2727-2737.
[4]	ZHU J, DING X, LI Z , et al. High-rise Building Layover Exploitation with Non-local Frequency Estimation in SAR Interferograms[J]. Remote Sensing, 2017,9(6):579.
[5]	DUBOIS C, THIELE A, HINZ S . Building Detection and Building Parameter Retrieval in InSAR Phase Images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016,114:228-241.
[6]	GUO R, WANG F, ZANG B , et al. High-rise Building 3D Reconstruction with the Wrapped Interferometric Phase[J]. Sensors, 2019,19(6):1439-1453.
[7]	GUOR, ZHU X X. High-rise Building Feature Extraction Using High Resolution Spotlight TanDEM-X Data [C]//Proceedings of the 2014 European Conference on Synthetic Aperture Radar. Berlin: VDE VERLAG GMBH, 2014: 55-58.
[8]	ROMANO J M, ROSARIO D, NIVER E . Morphological Operators for Polarimetric Anomaly Detection[J]. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2014,7(2):664-677.
[9]	SPAGNOLINI U . 2-D Phase Unwrapping and Instantaneous Frequency Estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995,33(3):579-589.
[10]	DELEDALLE C A, DENIS L, TUPIN F . NL-InSAR: Nonlocal Interferogram Estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(4):1441-1452.
[11]	BAIER G, ROSSI C, LACHAISE M , et al. A Nonlocal InSAR Filter for High-resolution DEM Genaeration from TanDEM-X Interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018,56(11):6469-6483.

参数	高层建筑(区域一)						高层建筑(区域二)
参数	1	2	3	4	5		1	2	3(左侧)	3(右侧)	3
建筑方向角/(°)	52.91	137.20	5.91	9.16	6.34		168.69	170.89	77.47	147.53	66.37

实验区域	高层建筑编号	相干系数均值		归一化幅度均值
实验区域	高层建筑编号	精细化	最终的	精细化	最终的
区域一	1	0.827 2	0.908 4	0.005 2	0.007 2
	2	0.848 3	0.946 4	0.006 7	0.012 3
	3	0.799 7	0.926 1	0.012 2	0.019 3
	4	0.905 9	0.949 9	0.012 5	0.017 9
	5	0.946 6	0.964 6	0.014 9	0.018 1
区域二	1	0.877 2	0.946 1	0.011 3	0.017 3
	2	0.848 3	0.951 8	0.011 7	0.019 2
	3	0.710 9	0.951 4	0.008 0	0.015 9

实验区域	高层建筑编号	精细化	最终的
区域一	1	0.176 1	0.140 7
	2	0.234 1	0.158 7
	3	0.136 4	0.135 3
	4	0.101 5	0.101 0
	5	0.254 8	0.240 6
区域二	1	1.274 6	0.549 6
	2	0.636 9	0.396 6
	3	3.236 8	0.381 7

[1]	LIU Jieyi,GONG Maoguo,ZHAN Tao,LI Hao,ZHANG Mingyang. Method for discrimination of false targets in multistation radar systems based on the deep neural network [J]. Journal of Xidian University, 2021, 48(2): 133-138.
[2]	WEI Ziyu,YANG Xi,WANG Nannan,YANG Dong,GAO Xinbo. Reciprocal bi-directional generative adversarial network for cross-modal pedestrian re-identification [J]. Journal of Xidian University, 2021, 48(2): 205-212.
[3]	GUO Zekun,TIAN Long,HAN Ning,WANG Penghui,LIU Hongwei,CHEN Bo. Radar HRRP based few-shot target recognition with CNN-SSD [J]. Journal of Xidian University, 2021, 48(2): 7-14.
[4]	HAN Zhuoxi,WANG Feng,CHEN Pei,LI Zhuolun. Fuzzy data association algorithm assisted by historical features [J]. Journal of Xidian University, 2021, 48(2): 92-98.
[5]	WANG Xiaohua,YAO Pengchao,MA Liping,WANG Wenjie,ZHANG Lei. Algorithm for extraction of features of robot speech control in the factory environment [J]. Journal of Xidian University, 2020, 47(2): 16-22.
[6]	WANG Hongyan,ZHU Limin,ZHANG Panjie,LI Jinping. Method for the detection of the piston side defect based on external contour registration [J]. Journal of Xidian University, 2019, 46(5): 75-83.
[7]	JING Guobin,LI Ning,SUN Guangcai,XING Mengdao. Very high resolution SAR imaging method combined with motion estimation [J]. Journal of Xidian University, 2019, 46(3): 1-7.
[8]	LIU Daohua,CUI Yushuang,ZHAO Yansong,SONG Yuting,WANG Jinghui. Method for retrieving the teaching image based on the improved convolutional neural network [J]. Journal of Xidian University, 2019, 46(3): 52-58.
[9]	YANG Jiangong,WANG Xili,LIU Shigang. Spectral-spatial classification of hyperspectral images using deep Boltzmann machines [J]. Journal of Xidian University, 2019, 46(3): 109-115.
[10]	WANG Xinyuan,XIAO Song,LI Lei,JIAO Lingling. Robust target tracking algorithm based on the ELM and discriminative correlation filter [J]. Journal of Xidian University, 2019, 46(1): 57-63.
[11]	WANG Xing;HUANG Xiaoyu;LIU Xuanpu;KONG Xianguang;NIU Meng. Multi-layer incremental feature extraction method for industrial big data [J]. Journal of Xidian University, 2018, 45(4): 106-111.
[12]	ZHANG Jinqiang;SUO Zhiyong;LI Zhenfang;BAO Zheng. Terrain elevation adaptive method for spaceborne InSAR coregistration [J]. Journal of Xidian University, 2018, 45(2): 31-36.
[13]	HE Wangpeng;SUN Wei;SU Bo;YANG Yunyi;GUO Baolong. Sparse feature extraction technique and its applications to machinery fault diagnosis [J]. Journal of Xidian University, 2018, 45(2): 154-159.
[14]	ZHANG Yuhe;GENG Guohua;WEI Xiaoran;SHI Chenchen;ZHANG Shunli. Feature extraction of point clouds using the DBSCAN clustering [J]. Journal of Xidian University, 2017, 44(2): 114-120.
[15]	ZHANG Jujie;FANG Min;GUO Jin. New method for multi-label feature extraction [J]. Journal of Xidian University, 2016, 43(6): 62-67.

Extraction of features of the urban high-rise building from high resolution InSAR data

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 11

Related Articles 15

Metrics

Comments

Recommended 10