Affinity Propagation for Hyperspectral Band Selection
نویسندگان
چکیده
منابع مشابه
Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images
Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to ...
متن کاملAdaptive affinity propagation with spectral angle mapper for semi-supervised hyperspectral band selection.
Band selection is a commonly used approach for dimensionality reduction in hyperspectral imagery. Affinity propagation (AP), a new clustering algorithm, is addressed in many fields, and it can be used for hyperspectral band selection. However, this algorithm cannot get a fixed number of exemplars during the message-passing procedure, which limits its uses to a great extent. This paper proposes ...
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While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy. In this paper we present a new methodology for combining unsupervised and supervised methods under classification accuracy and computational requirement ...
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In hyperspectral remote sensing, sensors acquire reflectance values at many different wavelength bands, to cover a complete spectral interval. These measurements are strongly correlated and no new information might be added when increasing the spectral resolution. Moreover, the higher number of spectral bands increases the complexity of a classification task. Therefore, feature extraction is a ...
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This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so...
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ژورنال
عنوان ژورنال: IOP Conference Series: Earth and Environmental Science
سال: 2018
ISSN: 1755-1307,1755-1315
DOI: 10.1088/1755-1315/170/2/022061