6533b86ffe1ef96bd12cdf9b
RESEARCH PRODUCT
Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution
Christine Fernandez-maloigneNoël RichardHermine ChatouxJon Yngve HardebergRui Jian Chusubject
Hyperspectral imagingbusiness.industryComputer scienceFeature extractionHyperspectral imagingPattern recognitionContext (language use)15. Life on landComputer Graphics and Computer-Aided DesignSupport vector machineGabor filtermetrologyJoint probability distributionFeature (computer vision)[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Artificial intelligencebusinessImage retrievaltextureSoftwaredescription
International audience; Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named relative spectral difference occurrence matrix (RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on HyTexiLa, content-based image retrieval (CBIR) on ICONES-HSI, and land cover classification on Salinas, RSDOM registers 98.5% accuracy, 80.3% precision (for the top 10 retrieved images), and 96.0% accuracy (after post-processing) respectively, outcompeting GLCM, Gabor filter, LBP, SVM, CCF, CNN, and GCN. Analysis shows the advantage of RSDOM in terms of feature size (a mere 126, 30, and 20 scalars using GMM in order of the three tasks) as well as metrological validity in texture representation regardless of the spectral range, resolution, and number of bands.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-04-12 |