Automatic radiometric normalization of multitemporal satellite imagery

By Morton John Canty, Allan Aasbjerg Nielsen, Michael Schmidt. 2004

366 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/8757cdcf725c5c3ad8e82ec30df9b970549db539

How researchers see this paper

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

In Analytical Chemistry

1. Jon Atli Benediktsson
2. Lorenzo Bruzzone
3. Gustau Camps-Valls
4. Devis Tuia
5. Warren B. Cohen
6. Allan Aasbjerg Nielsen
7. Zhiqiang Yang
8. Robert E. Kennedy

In Artificial Intelligence

1. Warren B. Cohen
2. Zhiqiang Yang
3. Robert E. Kennedy
4. Lorenzo Bruzzone
5. Devis Tuia
6. Todd A. Schroeder
7. Gustau Camps-Valls
8. Morton John Canty

In Media Technology

1. Lorenzo Bruzzone
2. Jon Atli Benediktsson
3. Devis Tuia
4. Gustau Camps-Valls
5. Liangpei Zhang
6. Zhiqiang Yang
7. Warren B. Cohen
8. Allan Aasbjerg Nielsen

Citations in the corpus

• Trajectory-based change detection for automated characterization of forest disturbance dynamics (2007, methodology): “All images were then radiometrically normalized using the multivariate alteration detection (MAD) algorithm of Canty et al. (2004), which provides a quick and effective statistical approach to relative normalization of satellite images (Schroeder et al., 2006).”
• Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation (2008, methodology): “This invariance is demonstrated explicitly in Canty et al. (2004).”
• Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon (2006, methodology): “ation was accomplished on a band-by-band basis using these "training" PIFs with reduced major axis (RMA) regression (Canty et al., 2004; Cohen et al., 2003).”
• Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms (2010, methodology): “All remaining images in theLTS of a single scenewere then normalized to the COST image using the MADCAL (multivariate alteration detection and calibration) algorithms of Canty et al. (2004), which identify stable pixelswithin a small subset of the larger image.”
• The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data (2007, neutral): “This is described in detail in [38].”
• Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods (2013, methodology): “Among these methods, we recall histogram matching, relative radiometric normalization of time series [34], and multivariate histogram matching [35].”
• A survey of image classification methods and techniques for improving classification performance (2007, neutral): “…and atmospheric normalization and correction (Markham and Barker 1987, Gilabert et al. 1994, Chavez 1996, Stefan and Itten 1997, Vermote et al. 1997, Tokola et al. 1999, Heo and FitzHugh 2000, Song et al. 2001, Du et al. 2002, McGovern et al. 2002, Canty et al. 2004, Hadjimitsis et al. 2004).”
• Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions (2013, methodology): “All the other images used were then normalized to the COST image using the MADCAL (multivariate alteration detection and calibration) algorithms of Canty et al. (2004), which identify stable pixels within a small subset of the larger image.”