This Title All WIREs
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 7.250

Critical insights into modern hyperspectral image applications through deep learning

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Hyperspectral imaging has shown tremendous growth over the past three decades. Hyperspectral imaging was evolved through remote sensing. Along, with the technological enhancements hyperspectral imaging has outgrown, conquering over other various application areas. In addition to it, data enriched data cubes with abundant spectral and spatial information works as perk for capturing, analyzing, reviewing, and interpreting results from data. This review concentrates on emerging application areas of hyperspectral imaging. Emerging application areas are selected in ways where there is a vast scope for future enhancements by exploiting cutting edge technology, that is, deep learning. Applications of hyperspectral imaging techniques in some selected areas (remote sensing, document forgery, history and archaeology conservation, surveillance and security, machine vision for fruit quality inspection, medical imaging) are focused. The review pivots around the publicly available datasets and features used domain wise. This review can act as a baseline for deep learning and machine vision experts, historical geographers, and scholars by providing them a view of how hyperspectral imaging is implemented in multiple domains along with future research prospects. This article is categorized under: Technologies > Machine Learning Technologies > Prediction
Emerging applications of hyperspectral imaging
[ Normal View | Magnified View ]
Accuracy trends analysis for remote sensing
[ Normal View | Magnified View ]
Distribution of papers reviewed domain wise
[ Normal View | Magnified View ]
Distribution of papers reviewed year‐wise
[ Normal View | Magnified View ]
Distribution of papers reviewed publication wise
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Prediction
Technologies > Machine Learning

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts