A spatial–spectral approach for deriving high signal qualityeigenvectors for remote sensing image transformations

Kurzfassung

Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformationof 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.