DOI: 10.1016/j.jag.2013.09.007
Scopus记录号: 2-s2.0-84897849064
论文题名: A spatial-spectral approach for deriving high signal quality eigenvectors for remote sensing image transformations
作者: Rogge D ; , Bachmann M ; , Rivard B ; , Nielsen A ; A ; , Feng J
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 26, 期: 1 起始页码: 387
结束页码: 398
语种: 英语
英文关键词: Eigenvector transformations
; Hyperspectral imaging
; Spatial and spectral processing
Scopus关键词: eigenvalue
; image analysis
; remote sensing
; spectral analysis
英文摘要: Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformation of the image data onto eigenvectors that comprise physically meaningful spectral properties (signal) canbe used to reduce the dimensionality of hyperspectral images as the number of spectrally distinct signalsources composing a given hyperspectral scene is generally much less than the number of spectral bands.Determining eigenvectors dominated by signal variance as opposed to noise is a difficult task. Problemsalso arise in using these transformations on large images, multiple flight-line surveys, or temporal datasets as computational burden becomes significant. In this paper we present a spatial-spectral approachto deriving high signal quality eigenvectors for image transformations which possess an inherently abil-ity to reduce the effects of noise. The approach applies a spatial and spectral subsampling to the data,which is accomplished by deriving a limited set of eigenvectors for spatially contiguous subsets. Thesesubset eigenvectors are compiled together to form a new noise reduced data set, which is subsequentlyused to derive a set of global orthogonal eigenvectors. Data from two hyperspectral surveys are used todemonstrate that the approach can significantly speed up eigenvector derivation, successfully be appliedto multiple flight-line surveys or multi-temporal data sets, derive a representative eigenvector set forthe full image data set, and lastly, improve the separation of those eigenvectors representing signal asopposed to noise. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79748
Appears in Collections: 气候变化事实与影响
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作者单位: German Remote Sensing Data Centre, DLR, Munchnerstr 20, D-82234, Germany; Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton T6G 2E3, Canada; Technical University of Denmark, National Space Institute, DK-2800 Kgs. Lyngby, Denmark
Recommended Citation:
Rogge D,, Bachmann M,, Rivard B,et al. A spatial-spectral approach for deriving high signal quality eigenvectors for remote sensing image transformations[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,26(1)