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Matches in SemOpenAlex for { <https://semopenalex.org/work/W2003182005> ?p ?o ?g. }

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• W2003182005 abstract "Hyperspectral remote sensing produces large volumes of data, quite often requiring hundreds of megabytes togigabytes of memory storage for a small geographical area for one-time data collection. Although the high spectral resolutionof hyperspectral data is quite useful for capturing and discriminating subtle differences in geospatial characteristics of thetarget, it contains redundant information at the band level. The objective of this study was to identify those bands that containthe most information needed for characterizing a specific geospatial feature with minimal redundancy. Band selection is performedwith both unsupervised and supervised approaches. Five methods (three unsupervised and two supervised) are proposedand compared to identify hyperspectral image bands to characterize soil electrical conductivity and canopy coveragein agricultural fields. The unsupervised approach includes information entropy measure and first and second derivativesalong the spectral axis. The supervised approach selects hyperspectral bands based on supplemental ground truth data usingprincipal component analysis (PCA) and artificial neural network (ANN) based models. Each hyperspectral image band wasranked using all five methods. Twenty best bands were selected by each method with the focus on soil and plant canopy characterizationin precision agriculture. The results showed that each of these methods may be appropriate for different applications.The entropy measure and PCA were quite useful for selecting bands with the most information content, while derivativemethods could be used for identifying absorption features. ANN measure was the most useful in selecting bands specific toa target characteristic with minimum information redundancy. The results also indicated that a combination of wavebandswith different bandwidths will allow use of fewer than 20 bands used in this study to represent the information contained inthe top 20 bands, thus reducing image data dimensionality and volume considerably." @default.
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• W2003182005 date "2004-01-01" @default.
• W2003182005 modified "2023-09-27" @default.
• W2003182005 title "HYPERSPECTRAL IMAGE DATA MINING FOR BAND SELECTION IN AGRICULTURAL APPLICATIONS" @default.
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• W2003182005 doi "https://doi.org/10.13031/2013.16087" @default.
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