Spectral classification of WorldView-2 multi-angle sequence

In this study, we investigate the ability of the spectral data from a multi-angle WorldView-2 image sequence to improve classification accuracy of an urban scene. Specifically, we investigate the multi-angle reflectance, as well as two data extraction methods applied to the reflectance data, developed from thirteen images collected over downtown Atlanta, GA in Dec. 2009. These images were collected sequentially by WorldView-2 within two minutes and range from approximately 30 degrees off-nadir southward to 30 degrees off-nadir northward.

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[3]  E. M. Wright,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[4]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[5]  Gail P. Anderson,et al.  Atmospheric correction for shortwave spectral imagery based on MODTRAN4 , 1999, Optics & Photonics.

[6]  Fabio Pacifici,et al.  On the relative predictive value of the new spectral bands in the WorldWiew-2 sensor , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[7]  David P. Miller,et al.  Status of atmospheric correction using a MODTRAN4-based algorithm , 2000, SPIE Defense + Commercial Sensing.

[8]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.