Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

Tang, Chang; Liu, Xinwang; Zhu, En; Wang, Lizhe; Zomaya, Albert

doi:10.24963/ijcai.2021/418

Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

Chang Tang, Xinwang Liu, En Zhu, Lizhe Wang, Albert Zomaya

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3038-3044.

https://doi.org/10.24963/ijcai.2021/418

Keywords

Machine Learning: Unsupervised Learning
Machine Learning Applications: Applications of Unsupervised Learning

Abstract

In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.

Chang Tang, Xinwang Liu, En Zhu, Lizhe Wang, and Albert Zomaya. 2021. Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3038–3044. https://doi.org/10.24963/ijcai.2021/418

@inproceedings{ijcai2021p418,
  title     = {Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering},
  author    = {Tang, Chang and Liu, Xinwang and Zhu, En and Wang, Lizhe and Zomaya, Albert},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {3038--3044},
  year      = {2021},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2021/418},
  url       = {https://doi.org/10.24963/ijcai.2021/418},
}

TY  - CPAPER
TI  - Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering
AU  - Tang, Chang
AU  - Liu, Xinwang
AU  - Zhu, En
AU  - Wang, Lizhe
AU  - Zomaya, Albert
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 3038
EP  - 3044
DO  - 10.24963/ijcai.2021/418
UR  - https://doi.org/10.24963/ijcai.2021/418
ER  -