Constrained band selection for hyperspectral imagery

By Chein-I. Chang, Su Wang. IEEE Transactions on Geoscience and Remote Sensing, 2006

495 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5d9eb3450441269f33c7efd7c74a266bb75c1736

How researchers see this paper

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

In Atmospheric Science

1. José M. Bioucas-Dias
2. Sen Jia
3. Weiwei Sun
4. Qian Du
5. Xuelong Li
6. Yuan Yuan
7. Guihua Tang
8. Qi Wang

In Computer Vision and Pattern Recognition

1. Qi Wang
2. Qian Du
3. Weiwei Sun
4. Xuelong Li
5. Yuan Yuan
6. Fahong Zhang
7. Jianzhe Lin
8. José M. Bioucas-Dias

In Media Technology

1. José M. Bioucas-Dias
2. Sen Jia
3. Xuelong Li
4. Qian Du
5. Yuan Yuan
6. Weiwei Sun
7. Jose M. P. Nascimento
8. Antonio J. Plaza

Citations in the corpus

• A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection (2016, methodology): “as projection pursuit [13], [14], principal component analysis (PCA) [15]–[17], and independent component analysis [18], whereas the latter identifies a subset of the original spectral bands that contains most of the characteristics [19].”
• Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking (2016, methodology): “Chang and Wang [26] propose a constrained energy”
• Hyperspectral Subspace Identification (2008, background): “Band selection or band extraction, as the name suggests, exploits the high correlation existing between adjacent bands to select a few spectral components among those with higher signal-to-noise ratio (SNR) [15]–[20].”
• Optimal Clustering Framework for Hyperspectral Band Selection (2018, methodology): “About the parameters among them, WaLuDi, UBS, VGBS and E-FDPC are parameter-free so only K is needed to be set.”
• Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, unknown): “These data sets have pixels and and the following characteristics: SusgsP5PPSNR30 � pure pixels and abundances uniformly distributed over the simplex (top left); SusgsP5SNR30 non pure pixels and abundances uniformly distributed over the simplex (top right); SusgsP5MP08SNR30 abundances uniformly dist”
• Hyperspectral Band Selection: A Review (2019, methodology): “In [25], band selection was implemented into a framework of target detection, where a band image was treated as a desired target signature and other bands were unknown signature vectors; the objective was to minimize the correlations of selected bands.”