| 书目名称 | Dimensionality Reduction of Hyperspectral Imagery | | 编辑 | Arati Paul,Nabendu Chaki | | 视频video | | | 概述 | 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 | | 图书封面 |  | | 描述 | 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.. | | 出版日期 | Book 2024 | | 关键词 | Dimensionality reduction; Hyperspectral image; Feature selection; Feature extraction; Band optimization; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-42667-4 | | isbn_softcover | 978-3-031-42669-8 | | isbn_ebook | 978-3-031-42667-4 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
The information of publication is updating
|
|
发表于 2025-3-21 17:55:05
