Quantifying Sub-pixel Urban Impervious Surface through Fusion of Optical and InSAR Imagery

被引:40
作者
Yang, Limin
Jiang, Liming [1 ,2 ]
Lin, Hui [1 ]
Liao, Mingsheng [3 ]
机构
[1] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Shatin, Hong Kong, Peoples R China
[2] China State Key Lab Informat Engn Surveying Mappi, Wuhan, Peoples R China
[3] State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China
关键词
CLASSIFICATION; AREA;
D O I
10.2747/1548-1603.46.2.161
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
In this study, we explored the potential to improve urban impervious surface modeling and mapping with the synergistic use of optical and Interferometric Synthetic Aperture Radar (InSAR) imagery. We used a Classification and Regression Tree (CART) based approach to test the feasibility and accuracy of quantifying Impervious Surface Percentage (ISP) using four spectral bands of SPOT 5 high-resolution geometric (HRG) imagery and three parameters derived from the European Remote Sensing (ERS)-2 Single Look Complex (SLC) SAR image pair. Validated by an independent ISP reference dataset derived from the 33 cm-resolution digital aerial photographs, results show that the addition of InSAR data reduced the ISP modeling error rate from 15.5% to 12.9% and increased the correlation coefficient from 0.71 to 0.77. Spatially, the improvement is especially noted in areas of vacant land and bare ground, which were incorrectly mapped as urban impervious surfaces when using the optical remote sensing data. In addition, the accuracy of ISP prediction using InSAR images alone is only marginally less than that obtained by using SPOT imagery. The finding indicates the potential of using InSAR data for frequent monitoring of urban settings located in cloud-prone areas.
引用
收藏
页码:161 / 171
页数:11
相关论文
共 17 条
[1]  
[Anonymous], P 7 INT S SPAT ACC A
[2]   Impervious surface coverage - The emergence of a key environmental indicator [J].
Arnold, CL ;
Gibbons, CJ .
JOURNAL OF THE AMERICAN PLANNING ASSOCIATION, 1996, 62 (02) :243-258
[3]  
Bauer ME, 2008, T&F SER REMOTE SENS, P3
[4]  
Breiman, 1984, OLSHEN STONE CLASSIF, DOI [10.2307/2530946, DOI 10.2307/2530946]
[5]   An advanced system for the automatic classification of multitemporal SAR images [J].
Bruzzone, L ;
Marconcini, M ;
Wegmüller, U ;
Wiesmann, A .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (06) :1321-1334
[6]  
Flanagan M., 2001, P 2001 ASPRS ANN CON
[7]   SAR applications in human settlement detection, population estimation and urban land use pattern analysis: A status report [J].
Henderson, FM ;
Xia, ZG .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1997, 35 (01) :79-85
[8]   Urban change detection based on coherence and intensity characteristics of SAR imagery [J].
Liao, Mingsheng ;
Jiang, Liming ;
Lin, Hui ;
Huang, Bo ;
Gong, Jianya .
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2008, 74 (08) :999-1006
[9]   Use of impervious surface in urban land-use classification [J].
Lu, Dengsheng ;
Weng, Qihao .
REMOTE SENSING OF ENVIRONMENT, 2006, 102 (1-2) :146-160
[10]  
Schuler T., 1994, Watershed Protection, V1, P1, DOI DOI 10.9774/GLEAF.978-1-909493-38-4_2