Title :
Tensor-Driven Hyperspectral Denoising: A Strong Link for Classification Chains?
Author :
Martín-Herrero, Julio ; Ferreiro-Armán, Marcos
Author_Institution :
Dept. Signal Theor. & Commun., Univ. of Vigo, Vigo, Spain
Abstract :
We show how a tensor-driven anisotropic diffusion denoising method affects the performance of a classifier trained to discriminate among vine varieties in noisy hyper spectral images. We compare the classification statistics on the original and denoised images and discuss the convenience of this kind of preprocessing for classification in hyperspectral images.
Keywords :
image classification; image denoising; tensors; classification chain; classification statistics; denoised image; hyperspectral image classification; noisy hyper spectral image; tensor-driven anisotropic diffusion denoising; tensor-driven hyperspectral denoising; Anisotropic magnetoresistance; Hypercubes; Hyperspectral imaging; Noise reduction; Pixel; Support vector machines; anisotropic diffusion; denoising; hyperspectral imaging;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.691
