IEEE Trans Image Process - Determining the intrinsic dimension of a hyperspectral image using random matrix theory.

Tópicos

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Resumo

Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process and under- or overestimation of this number may lead to incorrect unmixing in unsupervised methods. In this paper, we discuss a new method for determining the intrinsic dimension using recent advances in random matrix theory. This method is entirely unsupervised, free from any user-determined parameters and allows spectrally correlated noise in the data. Robustness tests are run on synthetic data, to determine how the results were affected by noise levels, noise variability, noise approximation, and spectral characteristics of the endmembers. Success rates are determined for many different synthetic images, and the method is tested on two pairs of real images, namely a Cuprite scene taken from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) and SpecTIR sensors, and a Lunar Lakes scene taken from AVIRIS and Hyperion, with good results.

Resumo Limpo

determin intrins dimens hyperspectr imag import step spectral unmix process overestim number may lead incorrect unmix unsupervis method paper discuss new method determin intrins dimens use recent advanc random matrix theori method entir unsupervis free userdetermin paramet allow spectral correl nois data robust test run synthet data determin result affect nois level nois variabl nois approxim spectral characterist endmemb success rate determin mani differ synthet imag method test two pair real imag name cuprit scene taken airborn visibl infrar imag spectromet aviri spectir sensor lunar lake scene taken aviri hyperion good result

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