Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping

被引:56
作者
Ban, Yifang [1 ]
Jacob, Alexander [1 ]
机构
[1] KTH Royal Inst Technol, Div Geoinformat, S-10044 Stockholm, Sweden
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2013年 / 51卷 / 04期
关键词
Edge-aware region growing and merging (EARGM); ENVISAT advanced synthetic aperture radar (SAR) (ASAR); fusion; HJ-1B; multitemporal; segmentation; urban land cover; HIGH-RESOLUTION SAR; IMAGE SEGMENTATION; CLASSIFICATION; EDGE; EXTRACTION; AREAS; PATTERNS; CHINA;
D O I
10.1109/TGRS.2012.2236560
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The objectives of this research are to develop robust methods for segmentation of multitemporal synthetic aperture radar (SAR) and optical data and to investigate the fusion of multitemporal ENVISAT advanced synthetic aperture radar (ASAR) and Chinese HJ-1B multispectral data for detailed urban land-cover mapping. Eight-date multiangle ENVISAT ASAR images and one-date HJ-1B charge-coupled device image acquired over Beijing in 2009 are selected for this research. The edge-aware region growing and merging (EARGM) algorithm is developed for segmentation of SAR and optical data. Edge detection using a Sobel filter is applied on SAR and optical data individually, and a majority voting approach is used to integrate all edge images. The edges are then used in a segmentation process to ensure that segments do not grow over edges. The segmentation is influenced by minimum and maximum segment sizes as well as the two homogeneity criteria, namely, a measure of color and a measure of texture. The classification is performed using support vector machines. The results show that our EARGM algorithm produces better segmentation than eCognition, particularly for built-up classes and linear features. The best classification result (80%) is achieved using the fusion of eight-date ENVISAT ASAR and HJ-1B data. This represents 5%, 11%, and 14% improvements over eCognition, HJ-1B, and ASAR classifications, respectively. The second best classification is achieved using fusion of four-date ENVISAT ASAR and HJ-1B data (78%). The result indicates that fewer multitemporal SAR images can achieve similar classification accuracy if multitemporal multiangle dual-look-direction SAR data are carefully selected.
引用
收藏
页码:1998 / 2006
页数:9
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