Towards sea surface pollution detection from visible band images

Inna Stainvas, David Lowe

Research output: Contribution to journalArticlepeer-review


This paper presents a novel approach to water pollution detection from remotely sensed low-platform mounted visible band camera images. We examine the feasibility of unsupervised segmentation for slick (oily spills on the water surface) region labelling. Adaptive and non adaptive filtering is combined with density modeling of the obtained textural features. A particular effort is concentrated on the textural feature extraction from raw intensity images using filter banks and adaptive feature extraction from the obtained output coefficients. Segmentation in the extracted feature space is achieved using Gaussian mixture models (GMM).
Original languageEnglish
Pages (from-to)1848-1856
Number of pages9
JournalIEICE Transactions on Electronics
Issue number12
Publication statusPublished - Dec 2001


  • water pollution detection
  • unsupervised segmentation
  • filtering
  • Gaussian mixture models


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