Estimation of number of spectrally distinct signal sources in hyperspectral imagery

By Chein-I. Chang, Qian Du. IEEE Transactions on Geoscience and Remote Sensing, 2004

1,013 citations recorded by the source corpus.

Subfields: Computational Mechanics, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/25f7a27a2c8bb01df6937952645844ccc4dff89e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. José M. Bioucas-Dias
2. Antonio J. Plaza
3. Y. Qian
4. Jocelyn Chanussot
5. Jose M. P. Nascimento
6. Paul D. Gader
7. Naoto Yokoya
8. Sen Jia

In Media Technology

1. José M. Bioucas-Dias
2. Antonio J. Plaza
3. Jocelyn Chanussot
4. Jun Yu Li
5. Sen Jia
6. Y. Qian
7. Pedram Ghamisi
8. Qian Du

In Signal Processing

1. José M. Bioucas-Dias
2. Sen Jia
3. Y. Qian
4. Antonio J. Plaza
5. Jocelyn Chanussot
6. Jose M. P. Nascimento
7. Jun Yu Li
8. Hairong Qi

Citations in the corpus

• A New Growing Method for Simplex-Based Endmember Extraction Algorithm (2006, methodology): “In the SGA, a newly developed concept, called virtual dimensionality (VD) [10], [11], which has recently shown success in determination of the number of endmembers for endmember extraction [9], [12], [13], is proposed to estimate such p.”
• Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis (2006, methodology): “Unfortunately, as demonstrated in [4]–[6], this was not an effective measure.”
• Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing (2009, methodology): “Here, P is estimated by virtual dimensionality (VD) method [37].”
• Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “One of the most commonly used approaches to this problem is the virtual dimensionality (VD) method [135], which follows the pigeon-hole principle.”
• Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013, methodology): “One can conduct it by using virtual dimensionality [32] or HySime [33].”
• Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization (2007, methodology): “The estimated number of endmembers using the VD method [28] is equal to c = 9.”
• Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, positive): “These criteria have in fact been used in hyperspectral applications [96] adopting the approach introduced by Wax and Kailath in [97].”
• Hyperspectral Subspace Identification (2008, methodology): “In this paper, we follow a multiple regression theory-based approach [43], [54], which outperforms the shift difference method, which is used, for example, in the NAPC [24] algorithm.”
• Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis (2008, methodology): “The three VD methods in [17] provided different estimates.”
• Vertex component analysis: a fast algorithm to unmix hyperspectral data (2005, background): “In order to estimate the number of endmembers present in the processed area, we resort to the virtual dimensionality (VD), recently proposed in [61].”