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computers / comp.ai / Second Call for Book Chapters: Computational Intelligence based Hyperspectral Image Analysis, Springer (Scopus Indexed)

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o Second Call for Book Chapters: Computational Intelligence based Hyperspectral ImAnu Bajaj

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Second Call for Book Chapters: Computational Intelligence based Hyperspectral Image Analysis, Springer (Scopus Indexed)

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From: er.anubajaj@gmail.com (Anu Bajaj)
Newsgroups: comp.ai
Subject: Second Call for Book Chapters: Computational Intelligence based
Hyperspectral Image Analysis, Springer (Scopus Indexed)
Date: Fri, 14 Jul 2023 06:26:18 EDT
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 by: Anu Bajaj - Fri, 14 Jul 2023 10:26 UTC

**Second Call for Book Chapters - please circulate this CFP to your
colleagues and networks **

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".

It would help if you could let us know the tentative title of your
contribution within 10 days of receiving this mail so that we can plan /
structure the table of contents of the book.

Submission link: https://forms.gle/owMZQys1yd6zXtkMA

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 https://sites.google.com/view/cihia2023/home

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

Information contact: Anu Bajaj (er.anubajaj@gmail.com)


computers / comp.ai / Second Call for Book Chapters: Computational Intelligence based Hyperspectral Image Analysis, Springer (Scopus Indexed)

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