An end-user-oriented framework for RGB representation of multitemporal SAR images and visual data mining

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a new framework for the generation of two new classes of RGB products derived from multitemporal SAR data. The aim of our processing chain is to provide products characterized by a high degree of interpretability (thanks to a consistent rendering of the underlying electromagnetic scattering mechanisms) and by the possibility to be exploited in combination with simple algorithms for information extraction. The physical rationale of the proposed RGB products is presented through examples highlighting their principal properties. Finally, the suitability of these products with applications is demonstrated through two examples dealing with feature extraction and classification activities.

Original languageEnglish
Title of host publicationProceedings Volume 10004
Subtitle of host publicationImage and Signal Processing for Remote Sensing XXII
EditorsLorenzo Bruzzone, Francesca Bovolo
PublisherSPIE
Number of pages7
Volume10004
ISBN (Electronic)9781510604124
DOIs
Publication statusPublished - 18 Oct 2016
EventImage and Signal Processing for Remote Sensing XXII - Edinburgh, UK United Kingdom
Duration: 26 Sep 201628 Sep 2016

Conference

ConferenceImage and Signal Processing for Remote Sensing XXII
CountryUK United Kingdom
CityEdinburgh
Period26/09/1628/09/16

Keywords

  • classification
  • image enhancement
  • Level-1α products
  • Level-1β products
  • multitemporal SAR
  • self-organizing maps
  • synthetic aperture radar

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Amitrano, D., Cecinati, F., Di Martino, G., Iodice, A., Mathieu, P. P., Riccio, D., & Ruello, G. (2016). An end-user-oriented framework for RGB representation of multitemporal SAR images and visual data mining. In L. Bruzzone, & F. Bovolo (Eds.), Proceedings Volume 10004: Image and Signal Processing for Remote Sensing XXII (Vol. 10004). [100040Y] SPIE. https://doi.org/10.1117/12.2241257