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

Submission Guidelines

Editors

Ajith Abraham 

Bennett University, Greater Noida, India; Machine Intelligence Research Labs (MIR Labs), USA

abraham.ajith@gmail.com

Anu Bajaj

Thapar Institute of Engineering and Technology, Patiala, Punjab, India

er.anubajaj@gmail.com