Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery

被引:45
作者
Hu, Bin [1 ]
Xu, Yongyang [2 ]
Huang, Xiao [3 ]
Cheng, Qimin [4 ]
Ding, Qing [1 ]
Bai, Linze [1 ]
Li, Yan [5 ]
机构
[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
[2] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China
[3] Univ Arkansas, Dept Geosci, Fayetteville, AR 72701 USA
[4] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, 1037 Luoyu Rd, Wuhan 430074, Peoples R China
[5] Inner Mongolia Elect Informat Vocat Tech Coll, Sch Elect & Automat, Hohhot 010070, Peoples R China
基金
中国国家自然科学基金;
关键词
Sentinel-2B; Sentinel-1A; land cover classification; support vector machine; data fusion; SPECTRAL-SPATIAL CLASSIFICATION; EXTREME-LEARNING-MACHINE; TIME-SERIES; IMPERVIOUS SURFACES; SENSED DATA; LONG-TERM; SAR; WUHAN; FORESTS; INTEGRATION;
D O I
10.3390/ijgi10080533
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.
引用
收藏
页数:16
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