doi:10.3808/jei.200500058
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Alternate Neural Network Models as Supervised Classifiers for Satellite Data

P. S. Sangle1* and S. M. George2

  1. Information Technology, National Institute of Industrial Engineering, Vihar Lake, Powai, Mumbai 400087, India
  2. Care Sustainability, A-29, AWHO, Dara Enclave, Sector 9, Nerul, New Mumbai 400 706, India

*Corresponding author. Email: purnima@nitie.edu

Abstract


Investigations on the effectivity of different neural network architectures, viz. number of hidden neurons, constrained neuronal connections (hierarchical network), and fuzzy aggregation based synaptic neuronal functions (fuzzy neural network) for satellite data classification are presented. Performance of networks trained with varied number of training sizes for classification in large spatial extensions are used as illustration through two case studies, viz. land use/land cover classification of Delhi Ridge and species classification of floral resources in Shimla and Chopal regions in India. The results have been compared with statistical methods.

Keywords: Floral species classification, fuzzy neural network, hierarchical network, land use classification, neural network, satellite imagery


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