6533b873fe1ef96bd12d55d0

RESEARCH PRODUCT

Kernel image similarity criterion

Vicent TalensJose MorenoGustau Camps-valls

subject

Estimation theorybusiness.industryHyperspectral imagingPattern recognitionGrayscaleNonlinear systemKernel methodSimilarity criterionKernel (image processing)Computer Science::Computer Vision and Pattern RecognitionArtificial intelligencebusinessImage resolutionMathematics

description

This paper presents a family of metrics for assessing image similarity. The methods use the Hilbert-Schmidt Independence Criterion (HSIC) to estimate nonlinear statistical dependence between multidimensional images. The proposed methods have very good theoretical and practical properties. We illustrate the performance in evaluating the quality of natural photographic images, hyperspectral images under different noise levels, in synthetic multiresolution problems, and real pansharpening products.

yearjournalcountryeditionlanguage
2011-07-012011 IEEE International Geoscience and Remote Sensing Symposium
https://doi.org/10.1109/igarss.2011.6049181