Hyperspectral imaging for astronomy and space surviellance

By E. Hege, Daniel G. O'Connell, W. Johnson, S. Basty, E. Dereniak. SPIE Optics + Photonics, 2004

189 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/37051b232c6d74444223d01734c81c3257bf1396

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. G. Wang

In Atomic and Molecular Physics, and Optics

1. G. Wang

Citations in the corpus

• Deep Learning-Based Classification of Hyperspectral Data (2014, background): “An incomplete list includes agriculture [3], mineralogy [4], surveillance [5], physics [6], astronomy [7], chemical imaging [8], and environmental sciences [9], [10].”
• Sparse Recovery of Hyperspectral Signal from Natural RGB Images (2016, background): “…Systems (HISs) has been limited to those domains and applications in which these aspects of the signal (either spatial, but mostly temporal resolution) were not central – remote sensing (cf. [2]), agriculture (cf. [3]), geology (cf. [4]), astronomy (cf. [5]), earth sciences (cf. [6]), and others.”
• Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015, unknown): “For this reason, hyperspectral data are used in a wide range of applications such as agriculture [3], mineralogy [4], surveillance [5], astronomy [7], and environmental sciences [9], [10].”