Hyperspectral imagery (HSI) has high spectral dimensionality which presents a serious challenge to HSI processing, and so reduction of dimensionality is necessary. Band selection (BS) is one of the categories of dimensionality reduction methods. Existing BS methods have expensive cost, need prior information or only cater for classification. In order to get an efficient and unsupervised BS method for spectral unmixing, two aspects work are done. First, original N-FINDR algorithm is greatly improved by substituting volume calculation for distance test. Second, the improved N-FINDR algorithm is used to construct an unsupervised BS method for spectral unmixing. Both theory and experiments prove that the new unsupervised BS method is very effective.
Unsupervised band selection method based on improved N-FINDR algorithm for spectral unmixing
2006-01-01
2429328 byte
Conference paper
Electronic Resource
English
Spectral unmixing algorithms based on statistical models [2480-03]
British Library Conference Proceedings | 1995
|Joint Blind Deconvolution and Spectral Unmixing of Hyperspectral Images
British Library Conference Proceedings | 2014
|Spectral-Spatial Joint Sparsity Unmixing of Hyperspectral Data using Overcomplete Dictionaries
German Aerospace Center (DLR) | 2014
|