Year: 2024
Type: article
Abstract: This paper delves into the considerable challenges of working with hyperspectral images, which are notably large and multidimensional, with file sizes often surpassing hundreds of megabytes. Hyperspectral imaging captures light across a continuous range of wavelengths, providing detailed spectral information for each pixel. This rich dataset is invaluable for applications such as environmental monitoring, precision agriculture, mineral exploration, and medical diagnostics, where accurate spectral data aids in identifying materials and detecting subtle variations. However, the immense data volume not only strains storage and transmission resources but also requires efficient processing and analysistechniques to handle the high-dimensional data without compromising quality. Additionally, compression methods are essential to manage storage constraints and improve real-time usability, but they must balance data reduction with the preservation of spectral integrity for effective analysis and application.
Authors: Sujendra G Bharadwaj, Shruthi B
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404698863