Unsupervised hyperspectral band selection via multi-feature information-maximization clustering
| Autor: | Marco Bevilacqua, Yannick Berthoumieu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science Feature vector 0211 other engineering and technologies Hyperspectral imaging Pattern recognition 02 engineering and technology Mutual information Data modeling Data set ComputingMethodologies_PATTERNRECOGNITION Kernel (image processing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Cluster analysis 021101 geological & geomatics engineering Curse of dimensionality |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip.2017.8296339 |
| Popis: | This paper presents a new approach for unsupervised band selection in the context of hyperspectral imaging. The hyperspectral band selection (HBS) task is considered as a clustering problem: bands are clustered in the image space; one representative image is then kept for each cluster, to be part of the set of selected bands. The proposed clustering method falls into the family of information-maximization clustering, where mutual information between data features and cluster assignments is maximized. Inspired by a clustering method of this family, we adapt it to the HBS problem and extend it to the case of multiple image features. A pixel selection step is also integrated to reduce the spatial support of the feature vectors, thus mitigating the curse of dimensionality. Experiments with different standard data sets show that the bands selected with our algorithm lead to higher classification performance, in comparison with other state-of-the-art HBS methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|