Hyperspectral imaging analysis aims at the estimation of the number of constituent substances, known as endmembers, their spectral signatures as well as their abundance fractions ...
Hyperspectral imaging is a promising tool for applications in geosensing, cultural heritage and beyond. However, compared to current RGB cameras, existing hyperspectral cameras ar...
Rei Kawakami, John Wright, Yu-Wing Tai, Yasuyuki M...
In this study, the authors investigate the use of hyperspectral imaging for food crop monitoring and contamination detection and characterization. The authors investigate the use ...
Terrance West, Lori M. Bruce, Saurabh Prasad, Dani...
Automated classification of land cover types based on hyperspectral imagery often involves a large geographical area, but class labels are available for only small portions of the...
Hyperspectral imaging segmentation has been an active research area over the past few years. Despite the growing interest, some factors such as high spectrum variability are still...
Silvia Valero, Philippe Salembier, Jocelyn Chanuss...
Maximum likelihood supervised classifications with 1-m 128 band hyperspectral data accurately map in-stream habitats in the Lamar River, Wyoming with producer's accuracies of ...
Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available ...
Baofeng Guo, Steve R. Gunn, Robert I. Damper, Jame...
Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied ...
Recent advances in space and computer technologies are revolutionizing the way remotely sensed data is collected, managed and interpreted. In particular, NASA is continuously gath...
Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of t...