A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection

By Sen Jia, Guihua Tang, Jiasong Zhu, Qingquan Li. IEEE Transactions on Geoscience and Remote Sensing, 2016

340 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/6a8520b2a55ceb2438ddce4192d0ba39f9d215bf

How researchers see this paper

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

In Analytical Chemistry

1. Qian Du
2. Qi Wang
3. Weiwei Sun
4. Xuelong Li
5. Fahong Zhang

In Biomedical Engineering

1. Qian Du
2. Xuelong Li

In Media Technology

1. Weiwei Sun
2. Xuelong Li
3. Qi Wang
4. Qian Du
5. Fahong Zhang
6. Nassim Ammour
7. José M. Bioucas-Dias
8. Antonio J. Plaza

Citations in the corpus

• Optimal Clustering Framework for Hyperspectral Band Selection (2018, background): “form band selection (UBS) [27], VGBS [34], E-FDPC [29], Fig.”
• Hyperspectral Band Selection: A Review (2019, background): “The E-FDPC algorithm computed the ranking score of each band by weighting the local density and the intracluster distance, and an exponentialbased learning rule was introduced to properly determine the cutoff threshold.”