Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis

By Qian Du, He Yang. IEEE Geoscience and Remote Sensing Letters, 2008

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Feature Mining for Hyperspectral Image Classification (2013, background): “To address this problem, new features that are dissimilar to the already selected subset can be selected one by one [56] via linear prediction (LP) error analysis.”
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection (2016, background): “The other aspect is related to the cutoff threshold parameter that is unchanged with the increase in the cluster number, which eventually degrades the representativeness of the chosen cluster centers [26].”
Hyperspectral Band Selection: A Review (2019, methodology): “In [45], linear prediction (LP) and OSP were adopted to jointly evaluate the similarity between a single band and multiple bands, and the selected bands were expected to achieve satisfactory performance in target detection and classification.”
Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking (2016, neutral): “Du and Yang [21] employ a strategy similar to that in [18]–[20].”
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification (2015, methodology): “Band selection, such as LPE [24], reduces the dimensionality by selecting a subset of spectral bands with distinctive and informative features.”