Hyperspectral pan-sharpening: A variational convex constrained formulation to impose parallel level lines, solved with ADMM

Autor: Alexis Huck, Francois de Vieilleville, Pierre Weiss, Manuel Grizonnet

Publikováno v: WHISPERS
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Jun 2014, Lausanne, France. pp.1-4, ⟨10.1109/WHISPERS.2014.8077650⟩

In this paper, we address the issue of hyperspectral pan-sharpening, which consists in fusing a (low spatial resolution) hyperspectral image HX and a (high spatial resolution) panchromatic image P to obtain a high spatial resolution hyperspectral ima

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36914e5a7b0f42b889b44d30c6a6df82
https://doi.org/10.1109/whispers.2014.8077650

Comparisonof local anomaly detection algorithms based on statistical hypothesis tests

Autor: Manuel Grizonnet, Mireille Guillaume, Guillaume Oller, Alexis Huck

Publikováno v: WHISPERS

We present some results about the comparison of two families of anomaly detection algorithms, specific to hyperspectral images analysis, both based on local statistical Hypothesis Testing (HT). The study has involved the RX and the Gauss-Markov Rando

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::42e1a03c9b87bae48b522dea5bd0e7f2
https://doi.org/10.1109/whispers.2011.6080940

Asymptotically CFAR Unsupervised Target Detection and Discrimination in Hyperspectral Images with Anomalous Component Pursuit (ACP)

Autor: Alexis Huck, Mireille Guillaume

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2010, 48 (11), pp 3980-3991. ⟨10.1109/TGRS.2010.2063434⟩
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2010, 48 (11), pp 3980-3991. ⟨10.1109/TGRS.2010.2063434⟩

International audience; This paper addresses the problem of anomaly detection in hyperspectral images. We propose and exploit a data model to establish the link between two main approaches in the area of anomaly detection, which are Hypothesis Testin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::625c81832dd1b324aa9f8c20ee852b28
https://hal.science/hal-00948179

Minimum Dispersion Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data

Autor: Alexis Huck, Jacques Blanc-Talon, Mireille Guillaume

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2010, PP (99), pp.1-13. ⟨10.1109/TGRS.2009.2038483⟩
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2010, PP (99), pp.1-13. ⟨10.1109/TGRS.2009.2038483⟩
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2010, 48 (6), pp.2590-2600. ⟨10.1109/TGRS.2009.2038483⟩
IEEE Transactions on Geoscience and Remote Sensing, 2010, 48 (6), pp.2590-2600. ⟨10.1109/TGRS.2009.2038483⟩

International audience; This paper considers the problem of unsupervised spectral unmixing for hyperspectral image analysis. Each observed pixel is assumed to be a noisy linear mixture of pure material spectra, namely endmembers. The mixing coefficie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d271d4e187d59441dec57a7fc441379
https://hal.science/hal-00471583

A CFAR algorithm for anomaly detection and discrimination in Hyperspectral Images

Autor: Mireille Guillaume, Alexis Huck

Publikováno v: Proceedings of IEEE International conference on Image Processing
IEEE International Conference on Image Processing October 12-15, ICIP 2008
IEEE International Conference on Image Processing October 12-15, ICIP 2008, Oct 2008, San Diego, United States. pp.1868-1871, ⟨10.1109/ICIP.2008.4712143⟩
ICIP

International audience; This paper proposes an anomaly detection algorithm for hyperspectral images. It is unsupervised (the researched spectra are not required a priori), discriminates the anomalies according to their spectra and has a Constant Fals

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9949ced7a1bdbc36fc92aef10c75a366
https://hal.archives-ouvertes.fr/hal-00471636