Description: Deep Learning for Hyperspectral Image Analysis and Classification Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Linmi Tao, Atif Mughees Format: Hardback Publisher: Springer Verlag, Singapore, Singapore Imprint: Springer Verlag, Singapore ISBN-13: 9789813344198, 978-9813344198 Synopsis This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
Price: 119.75 GBP
Location: Aldershot
End Time: 2025-02-03T10:58:30.000Z
Shipping Cost: 32.02 GBP
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Book Title: Deep Learning for Hyperspectral Image Analysis and Classification
Number of Pages: 207 Pages
Publication Name: Deep Learning for Hyperspectral Image Analysis and Classification
Language: English
Publisher: Springer Verlag, Singapore
Item Height: 235 mm
Subject: Engineering & Technology, Computer Science
Publication Year: 2021
Type: Textbook
Item Weight: 500 g
Author: Atif Mughees, Linmi Tao
Item Width: 155 mm
Series: Engineering Applications of Computational Methods
Format: Hardcover
