Feature Extraction From Multitemporal SAR Images Using Selforganizing Map Clustering and Object-Based Image Analysis

Donato Amitrano, Francesca Cecinati, Gerardo Di Martino, Antonio Iodice, Pierre Philippe Mathieu, Daniele Riccio, Giuseppe Ruello

Research output: Contribution to journalArticle

7 Citations (Scopus)
41 Downloads (Pure)

Abstract

We introduce a new architecture for feature extraction from multitemporal synthetic aperture radar (SAR) data. Its the purpose is to combine classic SAR processing and geographical object-based image analysis to provide a robust unsupervised tool for information extraction from time series images. The architecture takes advantage from the characteristics of the recently introduced RGB products of the Level-1 α and Level-1β families, and employs self-organizing map clustering and object-based image analysis. In particular, the input products are clustered using color homogeneity and automatically enriched with a semantic attribute referring to clusters' color, providing a preclassification mask. Then, in the frame of an application-oriented object-based image analysis, opportune layers measuring scattering and geometric properties of candidate objects are evaluated, and an appropriate rule-set is implemented in a fuzzy system to extract the feature of interest. The obtained results have been compared with those given by existing techniques and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semiarid environment.

Original languageEnglish
Pages (from-to)1556 - 1570
Number of pages15
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume11
Issue number5
Early online date19 Mar 2018
DOIs
Publication statusPublished - 1 May 2018

Keywords

  • Classification
  • multitemporal
  • object-based image analysis (OBIA)
  • self-organizing maps (SOM)
  • synthetic aperture radar (SAR)

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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