114 related articles for article (PubMed ID: 37067972)
21. Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images.
Imbiriba T; Bermudez JC; Richard C; Tourneret JY
IEEE Trans Image Process; 2016 Mar; 25(3):1136-51. PubMed ID: 26685243
[TBL] [Abstract][Full Text] [Related]
22. Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry.
Drumetz L; Chanussot J; Jutten C; Ma WK; Iwasaki A
IEEE Trans Image Process; 2020 Feb; ():. PubMed ID: 32092005
[TBL] [Abstract][Full Text] [Related]
23. Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery.
Eches O; Dobigeon N; Mailhes C; Tourneret JY
IEEE Trans Image Process; 2010 Jun; 19(6):1403-13. PubMed ID: 20215083
[TBL] [Abstract][Full Text] [Related]
24. Incorporating Spatial Information and Endmember Variability Into Unmixing Analyses to Improve Abundance Estimates.
Uezato T; Murphy RJ; Melkumyan A; Chlingaryan A
IEEE Trans Image Process; 2016 Dec; 25(12):5563-5575. PubMed ID: 27552754
[TBL] [Abstract][Full Text] [Related]
25. Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing.
Zouaoui A; Muhawenayo G; Rasti B; Chanussot J; Mairal J
IEEE Trans Image Process; 2023; 32():4649-4663. PubMed ID: 37552588
[TBL] [Abstract][Full Text] [Related]
26. Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity, or Mismodeling Effects.
Halimi A; Honeine P; Bioucas-Dias JM
IEEE Trans Image Process; 2016 Oct; 25(10):4565-79. PubMed ID: 27416597
[TBL] [Abstract][Full Text] [Related]
27. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing.
Hong D; Yokoya N; Chanussot J; Zhu XX
IEEE Trans Image Process; 2018 Nov; ():. PubMed ID: 30418901
[TBL] [Abstract][Full Text] [Related]
28. Optimizing the Endmembers Using Volume Invariant Constrained Model.
Geng X; Sun K; Ji L; Zhao Y; Tang H
IEEE Trans Image Process; 2015 Nov; 24(11):3441-9. PubMed ID: 26087488
[TBL] [Abstract][Full Text] [Related]
29. Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification.
Hao Wu ; Prasad S
IEEE Trans Image Process; 2018 Mar; 27(3):1259-1270. PubMed ID: 29990156
[TBL] [Abstract][Full Text] [Related]
30. Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability.
Brezini SE; Deville Y
Sensors (Basel); 2023 Feb; 23(4):. PubMed ID: 36850938
[TBL] [Abstract][Full Text] [Related]
31. Illumination invariant hyperspectral image unmixing based on a digital surface model.
Uezato T; Yokoya N; He W
IEEE Trans Image Process; 2020 Jan; ():. PubMed ID: 31940537
[TBL] [Abstract][Full Text] [Related]
32. [An algorithm of spectral minimum shannon entropy on extracting endmember of hyperspectral image].
Yang KM; Liu SW; Wang LW; Yang J; Sun YY; He DD
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Aug; 34(8):2229-33. PubMed ID: 25474967
[TBL] [Abstract][Full Text] [Related]
33. Automatic extraction of optimal endmembers from airborne hyperspectral imagery using iterative error analysis (IEA) and spectral discrimination measurements.
Song A; Chang A; Choi J; Choi S; Kim Y
Sensors (Basel); 2015 Jan; 15(2):2593-613. PubMed ID: 25625907
[TBL] [Abstract][Full Text] [Related]
34. Spectral Unmixing of Hyperspectral Remote Sensing Imagery via Preserving the Intrinsic Structure Invariant.
Shao Y; Lan J; Zhang Y; Zou J
Sensors (Basel); 2018 Oct; 18(10):. PubMed ID: 30340435
[TBL] [Abstract][Full Text] [Related]
35. Framelet-Based Sparse Unmixing of Hyperspectral Images.
Zhang G; Xu Y; Fang F
IEEE Trans Image Process; 2016 Apr; 25(4):1516-29. PubMed ID: 26849863
[TBL] [Abstract][Full Text] [Related]
36. Unsupervised Hyperspectral and Multispectral Images Fusion Based on Nonlinear Variational Probabilistic Generative Model.
Wang Z; Chen B; Zhang H; Liu H
IEEE Trans Neural Netw Learn Syst; 2022 Feb; 33(2):721-735. PubMed ID: 33136546
[TBL] [Abstract][Full Text] [Related]
37. [Effective endmembers based bilinear unmixing model].
Song MP; Zhang YR; An JB; Bao HM
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Jan; 34(1):196-200. PubMed ID: 24783560
[TBL] [Abstract][Full Text] [Related]
38. A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability.
Borsoi RA; Imbiriba T; Bermudez JCM
IEEE Trans Image Process; 2020 Jan; ():. PubMed ID: 31976890
[TBL] [Abstract][Full Text] [Related]
39. Spectral-Spatial Boundary Detection in Hyperspectral Images.
Al-Khafaji SL; Zhou J; Bai X; Qian Y; Liew AW
IEEE Trans Image Process; 2022; 31():499-512. PubMed ID: 34874859
[TBL] [Abstract][Full Text] [Related]
40. A maximum entropy approach to unsupervised mixed-pixel decomposition.
Miao L; Qi H; Szu H
IEEE Trans Image Process; 2007 Apr; 16(4):1008-21. PubMed ID: 17405433
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
