We use physical considerations to show that an affine transformation can be used to model the effect of environmental changes on hyperspectral image distributions. This allows the generation of a vector of moment invariants that describes an image distribution but does not depend on the environmental conditions. These vectors maintain the invariant property after each image band is spatially filtered which allows the representation to capture spatial properties. We use the distribution invariants and the Fisher discriminant to reduce the size of the representation by selecting optimized spectral bands. We apply the methods developed in this work to the illumination-invariant classification and recognition of regions in airborne images. We also show that the distribution transformation model can be used for change detection in regions viewed under unknown conditions.
We use physical considerations to show that an affine transformation can be used to model the effect of environmental changes on hyperspectral image distributions. This allows the generation of a vector of moment invariants that describes an image distribution but does not depend on the environmental conditions. These vectors maintain the invariant property after each image band is spatially filtered which allows the representation to capture spatial properties. We use the distribution invariants and the Fisher discriminant to reduce the size of the representation by selecting optimized spectral bands. We apply the methods developed in this work to the illumination-invariant classification and recognition of regions in airborne images. We also show that the distribution transformation model can be used for change detection in regions viewed under unknown conditions.
We use moment invariants for the recognition of regions in hyperspectral images under different illumination conditions. These moment invariants can be computed efficiently from the spectral histograms of the image regions. We propose methods for improving the recognition rate by choosing bands that improve the accuracy of the model underlying the invariants. These bands are also optimized to distinguish different materials. We demonstrate the use of multiple subsets of bands for invariant recognition. Experiments on DIRSIG images are presented to demonstrate the use of these methods.
We show that moment invariants of spectral distributions provide region descriptors that are independent of changes in the illumination and atmospheric conditions. These invariants can be computed efficiently for band subsets of hyperspectral data. Moment invariants have several applications in hyperspectral data processing. They can be used for the registration of image regions acquired at different times and for the transformation of images acquired at different times to a canonical invariant representation for comparison. Moment invariants can also be used for the recognition of image regions under unknown conditions. We demonstrate the properties of moment invariants using hyperspectral images synthesized using DIRSIG.
We develop a method for automatic end-member selection in hyperspectral images. The method models a hyperspectral pixel as a linear mixture of an unknown number of materials. In contrast to many end-member selection methods, the new method selects end-members based on the statistics of large numbers of pixels rather than attempting to identify a small number of the purest pixels. The method is based on maximizing the independence of material abundances at each pixel. We show how independent component analysis algorithms can be adapted for use with this problem. We show properties of the method by application to synthetic hyperspectral data.
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