03364nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001300153072001600166072001600182072002300198072001500221072001500236100007400251245011000325250001800435264007500453300006700528336002600595337002600621338003600647347002400683505027800707520125400985650002302239650004202262650002102304650003202325650003602357650004102393650006402434650003202498650003702530700007702567710003402644773002602678776003602704776003602740776003602776856004602812978-3-031-42667-4DE-He21320251030110820.0cr nn 008mamaa231004s2024    sz |    s    |||| 0|eng d  a97830314266747 a10.1007/978-3-031-42667-42doi 4aTK5102.9 7aTJF2bicssc 7aUYS2bicssc 7aTEC0670002bisacsh 7aTJF2thema 7aUYS2thema1 aPaul, Arati.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aDimensionality Reduction of Hyperspectral Imageryh[electronic resource] /cby Arati Paul, Nabendu Chaki.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXVIII, 116 p. 53 illus., 29 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aIntroduction -- Remote sensing -- Digital image processing -- Hyperspectral image characteristics -- Dimensionality reduction -- Dataset description -- Pooling based band extraction -- Ranking based band selection -- Band optimization -- Data Driven approach -- Conclusion.  aThis book provides information about different types of dimensionality reduction (DR) methods and their effectiveness in hyperspectral data processing. The authors first explain how hyperspectral imagery (HSI) plays an important role in remote sensing due to its high spectral resolution that enables better identification of different materials on the earth’s surface. The authors go on to describe potential challenges due to HSI being acquired in hundreds of narrow and contiguous bands, represented as a 3-dimensional image cube, often causing the bands to contain information redundancy. They then show how processing a large number of bands adds challenges in terms of computation complexity that reduces efficiency. The authors then present how DR is an essential step in hyperspectral data analysis to solve these issues. Overall, the book helps readers understand the DR processes and its impact in effective HSI analysis. Presents a data driven approach for dimensionality reduction (DR); Discusses the effect of spatial dimension and noise in the context of DR of hyperspectral imagery (HSI); Includes an optimization based approach for DR challenges and identification of gap areas in existing algorithms along with suitable solutions. 0aSignal processing. 0aImage processingxDigital techniques. 0aComputer vision. 0aComputational intelligence. 0aGeographic information systems.14aSignal, Speech and Image Processing.24aComputer Imaging, Vision, Pattern Recognition and Graphics.24aComputational Intelligence.24aGeographical Information System.1 aChaki, Nabendu.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303142666708iPrinted edition:z978303142668108iPrinted edition:z978303142669840uhttps://doi.org/10.1007/978-3-031-42667-4