LEADER 03808nam 22006615 450 001 9910760262403321 005 20231004093505.0 010 $a3-031-42667-3 024 7 $a10.1007/978-3-031-42667-4 035 $a(MiAaPQ)EBC30771293 035 $a(Au-PeEL)EBL30771293 035 $a(DE-He213)978-3-031-42667-4 035 $a(PPN)272920541 035 $a(CKB)28461821600041 035 $a(EXLCZ)9928461821600041 100 $a20231004d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDimensionality Reduction of Hyperspectral Imagery /$fby Arati Paul, Nabendu Chaki 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (125 pages) 311 08$aPrint version: Paul, Arati Dimensionality Reduction of Hyperspectral Imagery Cham : Springer International Publishing AG,c2023 9783031426667 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aSignal processing 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aComputational intelligence 606 $aGeographic information systems 606 $aSignal, Speech and Image Processing 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputational Intelligence 606 $aGeographical Information System 615 0$aSignal processing. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aComputational intelligence. 615 0$aGeographic information systems. 615 14$aSignal, Speech and Image Processing . 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputational Intelligence. 615 24$aGeographical Information System. 676 $a519.536 676 $a771.4 700 $aPaul$b Arati$01438724 702 $aChaki$b Nabendu 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910760262403321 996 $aDimensionality Reduction of Hyperspectral Imagery$93600360 997 $aUNINA