Call for Book Chapters
Book Title: Computational Intelligence based Hyperspectral Image Analysis
Description of the Book
Computational Intelligence (CI) based hyperspectral image analysis has gained significant importance in recent years due to its ability to extract valuable information from hyperspectral images and make predictions. Hyperspectral images provide a rich source of information about the composition and properties of objects in the environment. However, the vast amount of data generated by hyperspectral images can be overwhelming and hard to analyze. With their ability to provide valuable insights and improve decision-making, Computational Intelligence techniques act as a powerful tool that aids in automatic analysis and improves accuracy. Recent advances in the field have provided new and exciting ways to employ CI-based hyperspectral image analysis in many diverse applications.
The book aims to showcase these latest achievements and novel approaches in this field, focusing on their wide applications in agriculture, the environment, defense, medical diagnostics, food and product inspection, and mineral exploration. It will be an essential resource for those seeking to deepen their understanding of how hyperspectral image analysis can combine with computational intelligence techniques to solve specific tasks in different application fields from a multidisciplinary perspective.
We cordially invite researchers and scientists working in intelligent oncology care systems all around the globe to participate and submit their research work to contribute to our book.
Table of Contents
The topics include, but are not limited to:
Hyperspectral Image Acquisition
Hyperspectral Image Enhancement
Hyperspectral Image Clustering
Hyperspectral Image Representation
Hyperspectral Image Restoration
Hyperspectral Image Filtering
Hyperspectral Image Classification
Hyperspectral Image Segmentation
Hyperspectral Image Retrieval and Indexing
Hyperspectral Image Compression
Spatial/Spectral Super-Resolution
Computational Imaging
Object Detection
Applications in Remote Sensing
Multispectral/Hyperspectral Image Processing:
Band Selection
Dimensionality Reduction
Compressive Sensing
Sparse Representation
Image Registration/Matching
Image Denoising/Destriping
Image Fusion/Pansharpening
Unsupervised Learning, Semi-supervised Learning
Transfer Learning, Deep Learning on Hyperspectral Images
Real time Monitoring and applications
Publication
This book will be published in the Springer Series: Intelligent Systems Reference Library
Electronic ISSN: 1868-4408
Print ISSN: 1868-4394
Indexing: SCOPUS, SCImago, DBLP, zbMATH, Norwegian Register for Scientific Journals and Series
Important Dates
Full Chapter Submission Deadline August 30, 2023
Final Notification of Acceptance October 15, 2023
Final Chapter Submission Deadline November 15, 2023
Submission Guidelines
There is no limit on the chapter length.
Chapters must be formatted according to Springer format (Latex or Word).
The manuscript should be submitted in Word or Latex files.
The plagiarism rate should be less than 15%.
The figure should not have copyright issues; it can be redrawn or a copyright certificate should be obtained.
There is no processing or publication charge for this book.
Editors
Ajith Abraham
Bennett University, Greater Noida, India; Machine Intelligence Research Labs (MIR Labs), USA
Anu Bajaj
Thapar Institute of Engineering and Technology, Patiala, Punjab, India