Deep Learning for Hyperspectral Image Analysis and Classification / by Linmi Tao, Atif Mughees.

Format:

Book

Author/Creator:

Tao, Linmi, Author.

Mughees, Atif., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Engineering applications of computational methods 2662-3374 ; 5

Engineering Applications of Computational Methods, 2662-3374 ; 5

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Image processing-Digital techniques.

Computer vision.

Signal processing.

Machine Learning.

Artificial Intelligence.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Vision.

Signal, Speech and Image Processing .

Local Subjects:

Machine Learning.

Artificial Intelligence.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Vision.

Signal, Speech and Image Processing .

Physical Description:

1 online resource (XII, 207 pages) : 121 illustrations, 106 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

Introduction

Hyperspectral Imaging System

Classification Techniques for HSI

Preprocessing: Noise Reduction/ Band Categorization for HSI

Spatial Feature Extraction Using Segmentation

Multiple Deep learning models for feature extraction in classification

Deep learning for merging spatial and spectral information in classification

Sparse cording for Hyperspectral Data

Classification Applications of HSI classification

Conclusion.

Other Format:

Printed edition:

ISBN:

978-981-33-4420-4

9789813344204

Access Restriction:

Restricted for use by site license.