CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS

Authors

  • Katmoko Ari Sambodo Lembaga Penerbangan dan Antariksa Nasional (LAPAN)
  • Aniati Murni Faculty of Computer Science, University of Indonesia
  • Mahdi Kartasasmita Lembaga Penerbangan dan Antariksa Nasional (LAPAN)

Keywords:

Polarimetric-SAR, scattering model, freeman decomposition, Cloude decomposition, texture analysis, feature extraction, classification, neural networks

Abstract

This paper shows a study on an alternative method for classification of polarimetric-SAR data. The method is designed by integrating the comined features extracted from two scattering models(i.e., freeman decomposition model and cloud decomposition model) and textural analysis with distribution-free neural network classifier. The neural network classifier (wich is based on a feedforward back-propagation neural network architecture) properly exploits the information in the combined features for providing high accuracy classification result. The effectiveness of the proposed method is demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia.

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Published

2025-11-26

How to Cite

Katmoko Ari Sambodo, Aniati Murni, & Mahdi Kartasasmita. (2025). CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS. International Journal of Remote Sensing and Earth Sciences, 4, 1–17. Retrieved from https://ejournal.brin.go.id/ijreses/article/view/14115

Issue

Vol. 4 (2007)

Section

Articles

License

Copyright (c) 2007 Author (s)

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