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Dimensionality Reduction of Hyperspectral Imagery / / by Arati Paul, Nabendu Chaki



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Autore: Paul Arati Visualizza persona
Titolo: Dimensionality Reduction of Hyperspectral Imagery / / by Arati Paul, Nabendu Chaki Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024
Edizione: 1st ed. 2024.
Descrizione fisica: 1 online resource (125 pages)
Disciplina: 519.536
771.4
Soggetto topico: Signal processing
Image processing - Digital techniques
Computer vision
Computational intelligence
Geographic information systems
Signal, Speech and Image Processing
Computer Imaging, Vision, Pattern Recognition and Graphics
Computational Intelligence
Geographical Information System
Persona (resp. second.): ChakiNabendu
Nota di bibliografia: Includes bibliographical references and index.
Nota di contenuto: Introduction -- 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.
Sommario/riassunto: This 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.
Titolo autorizzato: Dimensionality Reduction of Hyperspectral Imagery  Visualizza cluster
ISBN: 3-031-42667-3
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910760262403321
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