Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes

By B. Datt, T. McVicar, T. V. Niel, D. Jupp, J. Pearlman. IEEE Transactions on Geoscience and Remote Sensing, 2003

511 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/184e378a7006900299a394c9699528f04196ba00

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In Artificial Intelligence

1. Yi Chen
2. Alex Held
3. Trac Duy Tran
4. Xin He
5. Yushi Chen
6. Zhouhan Lin
7. Nasser M. Nasrabadi

In Ecology

1. Christophe François
2. J. Pontailler
3. Stuart R. Phinn
4. Xin He
5. Nathalie Bréda
6. Yushi Chen
7. Eric Dufrene
8. Zhouhan Lin

In Environmental Engineering

1. Alex Held
2. Nathalie Bréda
3. Yushi Chen
4. Yang Shao

Citations in the corpus

• Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery (2004, methodology): “Prior to this conversion, the following pre-processing steps were implemented: re-calibration, band selection, destreaking, and repair of ‘bad’ (non-responsive) pixel values (figure 2) (Apan and Held 2002; Datt et al., 2003).”
• 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): “Data preprocessing generally includes initial channel selection, destriping, and atmospheric corrections (Datt et al., 2003).”
• Spatial-Spectral Transformer for Hyperspectral Image Classification (2021, background): “Thus, the obtained abundant information makes HSI useful in many areas including effective measurement of agricultural performance [3], plant diseases detection [4], identification of minerals [5], disease diagnosis and image-guided surgery [6], ecosystem measurement [7], and earth monitoring [8].”
• Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass (2008, methodology): “The destriping methodology used is a new and efficientone, that is improved from the “global method”described by Datt et al. (2003) .”
• Sparse Representation for Target Detection in Hyperspectral Imagery (2011, background): “HSI has found many applications in various fields such as military [2]–[4], agriculture [5], [6], and mineralogy [7].”