Analysis of Hyperspectral Data by Means of Transport Models and Machine Learning

Wojciech Czaja, Dong Dong, Pierre Emmanuel Jabin, Franck O.Ndjakou Njeunje

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

Abstract

We present a new physics-inspired method for analysis of hyperspectral imagery (HSI). The method is based on the concept of transport models for graphs. The proposed approach generalizes existing dimension reduction and feature extraction algorithms, by replacing the role of diffusion processes, as a measure of estimating proximity, with dynamical systems. This approach allows us to exploit different and new relationships within the complex data structures, such as those arising in HSI. We demonstrate this by proposing a specific multi-scale algorithm in which transport models are used to translate the information about contextual similarities of material classes to enhance feature extraction and classification results. This point is illustrated with a series of computational experiments.

Original languageEnglish (US)
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3680-3683
Number of pages4
ISBN (Electronic)9781728163741
DOIs
StatePublished - Sep 26 2020
Externally publishedYes
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: Sep 26 2020Oct 2 2020

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/TerritoryUnited States
CityVirtual, Waikoloa
Period9/26/2010/2/20

Keywords

  • advection
  • dimension reduction
  • feature extraction
  • machine learning
  • transport operator

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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