Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping

By F. Kruse, J. Boardman, Jonathan F. Huntington. IEEE Transactions on Geoscience and Remote Sensing, 2003

764 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/bb62afe2de3e3bbe69c207494be1328e5161f443

How researchers see this paper

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

In Artificial Intelligence

1. Dar A. Roberts
2. Philip E. Dennison
3. Antonio J. Plaza
4. Alfred Stein
5. Shunlin Liang
6. Jochem Verrelst
7. Kerry Halligan
8. Emmanuel John Muico Carranza

In Environmental Engineering

1. Philip E. Dennison
2. Dar A. Roberts
3. Jochem Verrelst
4. Michael E. Schaepman
5. Antonio J. Plaza
6. Alfred Stein
7. Susan L. Ustin
8. Angelo Palombo

In Media Technology

1. Dar A. Roberts
2. Philip E. Dennison
3. Kerry Halligan
4. Susan L. Ustin
5. Antonio J. Plaza
6. Shunlin Liang
7. Jochem Verrelst
8. Michael E. Schaepman

Citations in the corpus

• Earth system science related imaging spectroscopy — an assessment (2009, background): “…mapping can be identified as the most popular and successful application achieved using imaging spectroscopy, allowing various approaches to be implemented either focusing on the Earth (Clark et al., 1990; Cloutis, 1996; Kruse et al., 2003; Sabins, 1999) or other planets (Bibring et al., 2005).”
• QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management (2008, methodology): “Commonly used procedures in the “unmixing” of hyper-spectral data include: data preprocessing, feature selection and transformation, end-member selection, and the estimation of sub-pixel proportions (Kruse et al., 2003).”
• Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non‐spatial techniques (2014, background): “The unsatisfactory result for clay is probably because of the small SNR in the SWIR region of the Hyperion sensor (Kruse et al., 2003); the typical absorbance peaks of clay minerals are within this spectral range (about 2200 nm).”
• A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper (2004, methodology): “SAM has been applied to a similar range of applications, including mineral identification (Kruse et al., 2003), monitoring land cover change (Sohn et al., 1999), and discrimination of vegetation type (Lass et al., 2002; Silvestri et al., 2003).”
• Optimal field sampling for targeting minerals using hyperspectral data (2005, background): “As such, they allow identification of specific materials, whereas broadband multispectral data only allow discrimination between classes of materials (Kruse et al., 2003).”