Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification

	Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification
	Man, Qixia 1; Dong, Pinliang 1; Guo, Huadong 1
刊名	INTERNATIONAL JOURNAL OF REMOTE SENSING
	2015
卷号	36 期号:6 页码:2843-2866
通讯作者	Dong, PL (reprint author), Univ N Texas, Dept Geog, Denton, TX 76203 USA.
英文摘要	The complexity of urban areas makes it difficult for single-source remotely sensed data to meet all urban application requirements. Airborne light detection and ranging (lidar) can provide precise horizontal and vertical point cloud data, while hyperspectral images can provide hundreds of narrow spectral bands which are sensitive to subtle differences in surface materials. The main objectives of this study are to explore: (1) the performance of fused lidar and hyperspectral data for urban land-use classification, especially the contribution of lidar intensity and height information for land-use classification in shadow areas; and (2) the efficiency of combined pixel- and object-based classifiers for urban land-use classification. Support vector machine (SVM), maximum likelihood classification (MLC), and object-based classifiers were used to classify lidar, hyperspectral data and their derived features, such as the normalized digital surface model (nDSM), normalized difference vegetation index (NDVI), and texture measures, into 15 urban land-use classes. Spatial attributes and rules were used to minimize misclassification of the objects showing similar spectral properties, and accuracy assessments were carried out for the classification results. Compared with hyperspectral data alone, hyperspectral-lidar data fusion improved overall accuracy by 6.8% (from 81.7 to 88.5%) when the SVM classifier was used. Meanwhile, compared with SVM alone, the combined SVM and object-based method improved OA by 7.1% (from 87.6 to 94.7%). The results suggest that hyperspectral-lidar data fusion is effective for urban land-use classification, and the proposed combined pixel-and object-based classifiers are very efficient and flexible for the fusion of hyperspectral and lidar data.
研究领域[WOS]	Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000352030900007
内容类型	期刊论文
源URL	[http://ir.ceode.ac.cn/handle/183411/38516]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.[Man, Qixia] E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China 2.[Dong, Pinliang] Univ N Texas, Dept Geog, Denton, TX 76203 USA 3.[Guo, Huadong] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, Key Lab Digital Earth, Beijing 100094, Peoples R China
推荐引用方式 GB/T 7714	Man, Qixia,Dong, Pinliang,Guo, Huadong. Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2015,36(6):2843-2866.
APA	Man, Qixia,Dong, Pinliang,&Guo, Huadong.(2015).Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification.INTERNATIONAL JOURNAL OF REMOTE SENSING,36(6),2843-2866.
MLA	Man, Qixia,et al."Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification".INTERNATIONAL JOURNAL OF REMOTE SENSING 36.6(2015):2843-2866.