Optimized GLCM-based texture features for improved SAR-based flood mapping

A. Dasgupta, S. Grimaldi, R. Ramsankaran, J. P. Walker

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

3 Citations (Scopus)


Flood maps are indispensable to regional prioritization and effective resource distribution, and are required by policy makers, insurance firms, and disaster-relief agencies. SAR (Synthetic Aperture Radar) image classification is widely used for flood mapping, although the utilization of image texture has not been well explored. This study proposes a novel SAR-based flood mapping technique that uses optimized Gray Level Co-occurrence Matrix (GLCM)-based texture features, for more accurate flood-extent extraction from COSMO-SkyMed data. The approach involves the extraction of omnidirectional texture features through the use of an optimal window size, followed by independent component transform, which captures most of the information in the first three components and reduces data dimensionality. Flood maps that are derived using a support vector machine classifier were verified against aerial photographs. The presented approach increased the overall classification accuracy by nearly 1.5%.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781509049516
Publication statusPublished - 1 Dec 2017
EventIEEE International Geoscience and Remote Sensing Symposium 2017 - Fort Worth, United States of America
Duration: 23 Jul 201728 Jul 2017
Conference number: 37th

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


ConferenceIEEE International Geoscience and Remote Sensing Symposium 2017
Abbreviated titleIGARSS 2017
CountryUnited States of America
CityFort Worth
Internet address


  • COSMO-Skymed
  • Flood Mapping
  • SAR
  • SVM
  • Texture

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