Pattern Recognition of Remotely Sensed Data

  • Robert M. Haralick
Conference paper
Part of the NATO ASI Series book series (volume 4)


For us, Pattern Recognition refers to the automatic machine determination of salient patterns in remotely sensed image data. From the pattern recognition perspective, the world to be sensed is composed of units defined by the sensor. For digital imaging sensors, as a first approximation, the units can be thought of as small non-overlapping areas on the ground: one such area for each picture element (pixel) in the image. The sensor makes an ordered set of measurements on each unit sensed. The ordered set of measurements is called a measurement vector or measurement pattern. Each value measured in this set is a number proportional to the energy received by the sensor in some band of the electromagnetic spectrum at some specified observation time. The basic pattern recognition problem is first to automatically and consistently determine the informational class or category of each distinct region on the ground using the set of sensor measurement patterns and second to estimate the error rate for the automatically determined assignments.


Pattern Recognition Decision Rule Measurement Vector Economic Gain Pattern Discrimination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1983

Authors and Affiliations

  • Robert M. Haralick
    • 1
  1. 1.Department of Electrical EngineeringVirginia Polytechnic Institute and State UniversityBlacksburgUSA

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