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Remote Sensing Image Fusion Based on Adaptive RBF Neural Network

  • Yun Wen Chen
  • Bo Yu Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

With the availability of multi-sensor and multi-frequency image data from operational observation satellites, the fusion of image data has become an important tool in remote sensing image evaluation and segmentation. This paper presents a novel Radius Basis Function (RBF) neural network with some distinctive training strategies, which can integrate multiple information sources efficiently and exploit the potential advantages of each feature. Multi-scale features extracted from remote sensing images are evaluated adaptively and used for segmentation. Experimental results obtained on artificial and real data are both presented which demonstrate the effectiveness of our proposal.

Keywords

Image Fusion Synthetic Aperture Radar Synthetic Aperture Radar Image Hide Unit Feature Weight 
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

  • Yun Wen Chen
    • 1
  • Bo Yu Li
    • 1
  1. 1.Department of Computer Science and Engineering, School of Information Science and EngineeringFudan UniversityShanghaiChina

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