An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine

By Shijin Li, Hao Wu, D. Wan, Jiali Zhu. Knowledge-Based Systems, 2011

309 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/4f80c22efbb2d2483473beb83557909868b577a3

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. Weiwei Sun
2. Sonajharia Minz
3. Qian Du
4. Vipin Kumar

In Computer Vision and Pattern Recognition

1. Sonajharia Minz
2. Qian Du
3. Vipin Kumar
4. Weiwei Sun

In Media Technology

1. Weiwei Sun
2. Sonajharia Minz
3. Qian Du
4. Vipin Kumar
5. José M. Bioucas-Dias
6. Antonio J. Plaza
7. Wei Li
8. Jocelyn Chanussot

Citations in the corpus

• Feature Selection: A literature Review (2014, methodology): “Shijin Li, Hao Wu, Dingsheng, and Wan Jiali Zhu have developed a hybrid approach for feature selection using support vector machine and genetic algorithm [119].”
• Hyperspectral Band Selection: A Review (2019, methodology): “A hybrid band selection strategy [140] integrated clustering and searching schemes to search for the best combination of bands with higher classification accuracy.”