Optimal Clustering Framework for Hyperspectral Band Selection

By Qi Wang, Fahong Zhang, Xuelong Li. IEEE Transactions on Geoscience and Remote Sensing, 2018

334 citations recorded by the source corpus.

Band selection, by choosing a set of representative bands in a hyperspectral image, is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) an optimal clustering framework, which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint; 2) a rank on clusters strategy, which provides an effective criterion to select bands on existing clustering structure; and 3) an automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared with some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperforms the other methods on various data sets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/694f9c965b82ba993fdbfb7568f45b3577524c46

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In Media Technology

1. Ayesha Shafique
2. Guo Cao
3. Muhammad Aslam
4. Zia Khan
5. Muhammad Asad

Citations in the corpus

• Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “The high-dimensional remote sensing data HSI have limited accessibility to training samples, which makes deep neural networks, as a result, generally unsuccessful at generalizing the partitioning of HIS data during the training stage [82].”