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A Morphological Neural Network Approach for Vehicle Detection from High Resolution Satellite Imagery

  • Hong Zheng
  • Li Pan
  • Li Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

This paper introduces a morphological neural network approach to extract vehicle targets from high resolution panchromatic satellite imagery. In the approach, the morphological shared-weight neural network (MSNN) is used to classify image pixels on roads into vehicle targets and non-vehicle targets, and a morphological preprocessing algorithm is developed to identify candidate vehicle pixels. Experiments on 0.6 meter resolution QuickBird panchromatic data are reported in this paper. The experimental results show that the MSNN has a good detection performance.

Keywords

Road Segment Road Surface Vehicle Detection Vehicle Target Gray Level Histogram 
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 2006

Authors and Affiliations

  • Hong Zheng
    • 1
  • Li Pan
    • 2
  • Li Li
    • 1
  1. 1.Research Center for Intelligent Image Processing and Analysis, School of Electronic InformationWuhan UniversityWuhan, HubeiChina
  2. 2.School of Remote Sensing Information& EngineeringWuhan UniversityWuhan, HubeiChina

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