Deep Learning-Based Residual Control Chart for Binary Response

Autor: Jong Min Kim, Il Do Ha

Publikováno v: Symmetry, Vol 13, Iss 8, p 1389 (2021)

A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a

Externí odkaz: https://doaj.org/article/5ac375487b5c49cf84eab5941b9d6102

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Multi-resolution analysis techniques and nonlinear PCA for hybrid pansharpening applications

Autor: Gemine Vivone, Rocco Restaino, Mauro Dalla Mura, Jocelyn Chanussot, Giorgio Licciardi

Publikováno v: Multidimensional Systems and Signal Processing
Multidimensional Systems and Signal Processing, Springer Verlag, 2015, 27 (4), pp.807-830. ⟨10.1007/s11045-015-0359-y⟩

International audience; Hyperspectral images have a higher spectral resolution (i.e., a larger number of bands covering the electromagnetic spectrum), but a lower spatial resolution with respect to multispectral or panchromatic acquisitions. For incr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4645fd4120c2e3d8e673e756d05422d6
https://doi.org/10.1007/s11045-015-0359-y

Nonlinear Spectral Unmixing for the Characterisation of Volcanic Surface Deposit and Airborne Plumes from Remote Sensing Imagery

Autor: Chanussot, Giorgio Licciardi, Pasquale Sellitto, Alessandro Piscini, Jocelyn

Publikováno v: Geosciences; Volume 7; Issue 3; Pages: 46

In image processing, it is commonly assumed that the model ruling spectral mixture in a given hyperspectral pixel is linear. However, in many real life cases, the different objects and materials determining the observed spectral signatures overlap in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=multidiscipl::9bd0cd084dd2158d24f3607908c5eee1

Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability

Autor: Véronique Achard, Charlotte Revel, Yannick Deville, Xavier Briottet

Publikováno v: Advances in Principal Component Analysis
Advances in Principal Component Analysis, pp.225-252 (1-24), 2017, ⟨10.1007/978-981-10-6704-4_9⟩
Advances in Principal Component Analysis ISBN: 9789811067037

International audience; The most standard blind source separation (BSS) methods address the situation when a set of signals are available, e.g. from measurements, and all of them are linear memoryless combinations, with unknown coefficient values, of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17ad57968ae20065883c985648b379a1
https://hal.archives-ouvertes.fr/hal-01845155