An Informative Feature Selection Method Based on Sparse PCA for VHR Scene Classification

By Souleyman Chaib, Yanfeng Gu, Hongxun Yao. 2016

29 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Xiaoqiang Lu
4. Xuelong Li
5. Qi Wang
6. Jocelyn Chanussot
7. Shaoteng Liu
8. Nassim Ammour

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Xuelong Li
5. Jocelyn Chanussot
6. Qi Wang
7. Shaoteng Liu
8. Nassim Ammour

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, methodology): “In [126], Chaib et al. presented an informative feature selection method based on sparse PCA for high resolution satellite image scene classification.”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, methodology): “[37] present an informative feature selection method that applies a sparse principal component analysis to learn informative feature from a dictionary constructed with SIFT.”