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 theoriginal contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
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 theoriginal contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

Deep Learning for Hyperspectral Image Analysis and Classification
207
Deep Learning for Hyperspectral Image Analysis and Classification
207Paperback(1st ed. 2021)
Product Details
ISBN-13: | 9789813344228 |
---|---|
Publisher: | Springer Nature Singapore |
Publication date: | 02/21/2021 |
Series: | Engineering Applications of Computational Methods , #5 |
Edition description: | 1st ed. 2021 |
Pages: | 207 |
Product dimensions: | 6.10(w) x 9.25(h) x (d) |