Dimensionality Reduction of Hyperspectral Imagery
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...
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| Main Authors: | , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2024.
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| Edition: | 1st ed. 2024. |
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| Online Access: | https://doi.org/10.1007/978-3-031-42667-4 |
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| 100 | 1 | |a Paul, Arati. |e author. |0 (orcid)0000-0003-0422-5656 |1 https://orcid.org/0000-0003-0422-5656 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Dimensionality Reduction of Hyperspectral Imagery |h [electronic resource] / |c by Arati Paul, Nabendu Chaki. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XVIII, 116 p. 53 illus.). |b 29 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
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| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Geographic information systems. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing. |
| 650 | 2 | 4 | |a Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Geographical Information System. |
| 700 | 1 | |a Chaki, Nabendu. |e author. |0 (orcid)0000-0003-3242-680X |1 https://orcid.org/0000-0003-3242-680X |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031426667 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031426681 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031426698 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-42667-4 |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||