Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing

By Alina Zare, Dominic K. C. Ho. IEEE Signal Processing Magazine, 2014

365 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/24cce4a5602195c27a815dfa431e524738a386a4

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. Lin He
2. Xiuping Jia
3. Shutao Li
4. Chenying Liu
5. Jun Yu Li

In Artificial Intelligence

1. Xiuping Jia
2. Shutao Li
3. C. Zhao
4. Genping Zhao
5. Jun Yu Li
6. Tommaso Cai

In Media Technology

1. Shutao Li
2. Jun Yu Li
3. Lin He
4. Chenying Liu
5. Xiuping Jia
6. Leyuan Fang
7. Jon Atli Benediktsson
8. Yu Liu

Citations in the corpus

• Multiple endmembers based unmixing using Archetypal Analysis (2015, background): “Two main factors which affect hyperspectral mixed pixel unmixing are the sensor spatial resolution [1] and endmember variability of observed terrain [2].”
• Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, background): “With respect to the spectral variability, which is brought by many factors such as incident illumination, atmospheric effects, unwanted shade and shadow, natural spectrum variation, and instrument noises [2], [4], [10],”