DOI: 10.1016/j.jag.2012.10.004
Scopus记录号: 2-s2.0-84880251530
论文题名: Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies
作者: Forzieri G ; , Tanteri L ; , Moser G ; , Catani F
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2013
卷: 23, 期: 1 起始页码: 313
结束页码: 323
语种: 英语
英文关键词: ADS40
; Data fusion
; Land use/cover classification
; LiDAR
; MIVIS
; Urban/rural landscapes
Scopus关键词: accuracy assessment
; airborne sensing
; land cover
; land use planning
; landscape planning
; lidar
; maximum likelihood analysis
; rural planning
; satellite data
; satellite imagery
; spatial resolution
; urban planning
; Italy
; Marecchia River
英文摘要: The recent and forthcoming availability of high spatial resolution imagery from satellite and airborne sensors offers the possibility to generate an increasing number of remote sensing products and opens new promising opportunities for multi-sensor classification. Data fusion strategies, applied to modern airborne Earth observation systems, including hyperspectral MIVIS, color-infrared ADS40, and LiDAR sensors, are explored in this paper for fine-scale mapping of heterogeneous urban/rural landscapes. An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection). The analysis focuses on the identification of the best performing data fusion configuration and investigates sensor-derived marginal improvements. Numerical experiments, performed on a 20-km stretch of the Marecchia River (Italy), allow for a quantification of the synergies of multi-sensor airborne data. The use of Maximum Likelihood and of the feature space including ADS40, LiDAR derived normalized digital surface, texture layers, and 24 MIVIS bands represents the scheme that maximizes the classification accuracy on the test set. The best classification provides high accuracy (92.57% overall accuracy) and demonstrates the potential of the proposed approach to define the optimized data fusion and to capture the high spatial variability of natural and human-dominated environments. Significant inter-class differences in the identification schemes are also found by indicating possible sub-optimal solutions for landscape-driven mapping, such as mixed forest, floodplain, urban, and agricultural zones. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79811
Appears in Collections: 气候变化事实与影响
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作者单位: Climate Risk Management Unit, Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy; Department of Earth Sciences, University of Florence, Italy; Department of Telecommunications, Electronic, and Electrical Engineering, and Naval Architecture, University of Genoa, Italy
Recommended Citation:
Forzieri G,, Tanteri L,, Moser G,et al. Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies[J]. International Journal of Applied Earth Observation and Geoinformation,2013-01-01,23(1)