Dear Colleagues

We cordially invite researchers and scientists working in hyperspectral
image analysis all around the globe to participate and submit their
research work to contribute to our book titled "Computational Intelligence
based Hyperspectral Image Analysis".

Submission link:

Scope 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.

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

Important Dates:
Full Chapter Submission Deadline                     August 30, 2023
Final Notification of Acceptance                   October 15, 2023
Final Chapter Submission Deadline                     November 15, 2023

Publisher Details:
This book will be published in the Springer Series "Intelligent Systems
Reference Library" (Electronic ISSN: 1868-4408, Print ISSN: 1868-4394)

Indexed by: SCOPUS, SCImago, DBLP, zbMATH, Norwegian Register for
Scientific Journals and Series

Submission Guidelines:
The length of a book chapter should be between 20 and 30 pages.
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 any copyright issues; either it can be redrawn
or a copyright certificate should be obtained.
There is no processing or publication charge for this book.

More details on

Best Regards
Ajith Abraham, Flame University, Pune, India; Machine Intelligence Research
Labs (MIR Labs), USA
Anu Bajaj, Thapar Institute of Engineering and Technology, Patiala, Punjab,
Jyoti Maggu, Thapar Institute of Engineering and Technology, Patiala,
Punjab, India

Information contact: Anu Bajaj ([log in to unmask])



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