menu
{ "item_title" : "Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features", "item_author" : [" Subba Reddy Tatireddy", "Sai Chandana B", "Ravi Kumar Balleda "], "item_description" : "This book introduces hyperspectral remote sensing as a transformative imaging technology, capturing intricate details across multiple spectral bands. Originating from a doctoral thesis, the book bridges academic exploration and practical applications in hyperspectral image classification. It pioneers novel methodologies using deep learning and machine learning, featuring the Deep Adversarial Learning Framework for enhanced accuracy. The text explores groundbreaking approaches employing principal component analysis, empirical mode decomposition, and Support Vector Machines. A semi-supervised classification method inspired by Cycle-GANs is also presented. The book aims to offer a comprehensive understanding of hyperspectral imaging, its methodologies, and practical implications, serving as a valuable resource for students, researchers, and practitioners in the field.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/6/20/745/909/6207459091_b.jpg", "price_data" : { "retail_price" : "66.00", "online_price" : "66.00", "our_price" : "66.00", "club_price" : "66.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features|Subba Reddy Tatireddy

Mastering Hyperspectral Imaging using ML and Spatial-Spectral Features

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

This book introduces hyperspectral remote sensing as a transformative imaging technology, capturing intricate details across multiple spectral bands. Originating from a doctoral thesis, the book bridges academic exploration and practical applications in hyperspectral image classification. It pioneers novel methodologies using deep learning and machine learning, featuring the Deep Adversarial Learning Framework for enhanced accuracy. The text explores groundbreaking approaches employing principal component analysis, empirical mode decomposition, and Support Vector Machines. A semi-supervised classification method inspired by Cycle-GANs is also presented. The book aims to offer a comprehensive understanding of hyperspectral imaging, its methodologies, and practical implications, serving as a valuable resource for students, researchers, and practitioners in the field.

This item is Non-Returnable

Details

  • ISBN-13: 9786207459094
  • ISBN-10: 6207459091
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: January 2024
  • Dimensions: 9 x 6 x 0.27 inches
  • Shipping Weight: 0.39 pounds
  • Page Count: 112

Related Categories

You May Also Like...

    1

BAM Customer Reviews