Remote-Sensing Image Classification Based on an Improved Probabilistic Neural Network

  title={Remote-Sensing Image Classification Based on an Improved Probabilistic Neural Network},
  author={Yudong Zhang and Lenan Wu and Nabil Neggaz and Shuihua Wang and Geng Wei},
This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the GLCM-based texture features. Then, a probabilistic neural network (PNN) was adopted for classification, and a novel algorithm proposed to enhance its performance. Principle component analysis (PCA) was chosen to reduce feature dimensions, random division to reduce the number of neurons, and Brent's search (BS) to find the optimal bias values. The results… CONTINUE READING
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Classification of polarimetric SAR images of suburban areas using joint annealed segmentation and “H/A/α” polarimetric decomposition

  • T. M. Pellizzeri
  • ISPRS J. Photogramm. Remote Sens. 2003,
  • 2003
Highly Influential
5 Excerpts

Polarimetric synthetic aperture radar image classification by a hybrid method

  • K. U. Khan, J. Yang
  • Tsinghua Sci. Technol. 2007,
  • 2007
Highly Influential
6 Excerpts

An improved application technique of the adaptive probabilistic neural network for predicting concrete strength

  • J. J. Lee, D. Kim, S. K. Chang, C.F.M. Nocete
  • Comput. Mater. Sci. 2009,
  • 2009
1 Excerpt

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