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 a 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
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
Full Chapter Submission Deadline August 30, 2023
Final Notification of Acceptance October 15, 2023
Final Chapter Submission Deadline November 15, 2023
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
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
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 ([log in to unmask]