Object recognition and image segmentation: the Feature Analyst® approach

  • D. Opitz
  • S. Blundell
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

The collection of object-specific geospatial features, such as roads and buildings, from high-resolution earth imagery is a time-consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS). Traditional collection methods, such as hand-digitizing, are slow, tedious and cannot keep up with the ever-increasing volume of imagery assets. In this paper we describe the methodology underlying the Feature Analyst automated feature extraction (AFE) software, which addresses this core problem in GIS technology. Feature Analyst, a leading, commercial AFE software system, provides a suite of machine learning algorithms that learn on-the-fly how to classify object-specific features specified by an analyst. The software uses spatial context when extracting features, and provides a natural, hierarchical learning approach that iteratively improves extraction accuracy. An adaptive user interface hides the complexity of the underlying machine learning system while providing a comprehensive set of tools for feature extraction, editing and attribution. Finally, the system will automatically generate scripts that allow batch-processing of AFE models on additional sets of images to support large-volume, geospatial, data-production requirements.

Keywords

Automated Feature Extraction Machine Learning Geospatial Feature Collection 

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References

  1. Burl M (1998) Learning to recognize volcanoes. Machine Learning Journal. vol 30 (2/3).pp 165-194.CrossRefGoogle Scholar
  2. Brewer K, Redmond R, Winne K, Opitz D, and Mangrich M. (2005) Classifying and mapping wildfire severity, Photogramm Eng and Remote Sensing. vol 71, no 11.pp 1311-1320.Google Scholar
  3. Evangelia MT (2000) Supervised and unsupervised pattern recognition. CRC Press. Boca Raton, FL.Google Scholar
  4. Dietterich TG (2002) Ensemble learning. The Handbook of Brain Theory and Neural Networks. vol 2.pp 110-125.Google Scholar
  5. Kokiopoulou E and Frossard R (2006) Pattern detection by distributed feature extraction. IEEE International Conference on Image Processing.pp 2761-2764.Google Scholar
  6. McKeown et al., (1993) Research in automated analysis of remotely sensed imagery. Proc of the DARPA Image Understanding Workshop. Washington DC.Google Scholar
  7. McKeown D (1996) Top ten lessons learned in automated cartography. Technical Report CMU-CS-96-110. Computer science department. Carnegie Mellon University. Pittsburgh, PA.Google Scholar
  8. Mitchell T (1997) Machine learning. McGraw Hill. New York, NY.Google Scholar
  9. Nayar S, Poggio T (1996) Early visual learning. Oxford University Press. New York, NY.Google Scholar
  10. Nixon MS, Aguado AS (2002) Feature extraction and image processing. Elsevier. Amsterdam.Google Scholar
  11. Opitz D (1999) Feature selection for ensembles. Proc of the 16th National Conference on Artificial Intelligence.pp. 379-384.Google Scholar
  12. Opitz D, Bain W (1999) Experiments on learning to extract features from digital images. IASTED Signal and Image Processing.Google Scholar
  13. Opitz D, Blundell S (1999) An intelligent user interface for feature extraction from remotely sensed images. Proc. of the American Society for Photogrammetry and Remote Sensing.pp. 171-177.Google Scholar
  14. Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research. vol 11 (1).pp. 169-198.Google Scholar
  15. Opitz D, Shavlik J (1996) Generating accurate and diverse members of a neural-network ensemble. Advances in Neural Information Processing Systems. vol 8.pp. 535-541.Google Scholar
  16. Opitz D, Shavlik J (1999) Actively searching for an effective neural-network ensemble. Springer-Verlag Series on Perspective in Neural Computing.pp. 79-97.Google Scholar
  17. O’Brien M (2003) Feature extraction with the VLS Feature Analyst System. ASPRS International Conference. Anchorage, AK.Google Scholar
  18. Quinlan J (1993) C4.5: Programs for machine learning. Morgan Kaufmann. San Mateo, CA.Google Scholar
  19. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by backprapagation errors. Nature.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • D. Opitz
    • 1
  • S. Blundell
    • 1
  1. 1.Visual Learning SystemsMissoulaUSA

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