Sea-Land Clutter Segmentation Algorithm Based on Multi-measure Fusion with SVM Classifier

Kexin Li, Tao Shan*, Yushi Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Effectively segmenting sea clutter and land clutter in the sea-land junction area is of great significance for target detection and recognition on the sea surface. Existing sea-land clutter segmentation algorithms are mostly based on a single measure, of which the segmentation effect is not very satisfactory. In view of this problem, this paper proposes a novel sea-land clutter segmentation algorithm based on multi-measure fusion. Firstly, the characteristics of the clutter in the echo data collected by the sea detection radar are analyzed, and multiple appropriate segmentation measures are selected as feature vectors and fed into the Support Vector Machine (SVM) classifier. Then the classification result is converted into a binary image and processed by morphological filtering method to ensure the connectivity between the sea clutter area and the land clutter area. Finally, the feasibility and validity of the algorithm are verified by the real radar data.

Original languageEnglish
Title of host publication2021 13th International Conference on Communication Software and Networks, ICCSN 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages94-98
Number of pages5
ISBN (Electronic)9781665431828
DOIs
Publication statusPublished - 4 Jun 2021
Event13th International Conference on Communication Software and Networks, ICCSN 2021 - Chongqing, China
Duration: 4 Jun 20217 Jun 2021

Publication series

Name2021 13th International Conference on Communication Software and Networks, ICCSN 2021

Conference

Conference13th International Conference on Communication Software and Networks, ICCSN 2021
Country/TerritoryChina
CityChongqing
Period4/06/217/06/21

Keywords

  • multi-measure fusion
  • radar echo data
  • sea-land clutter segmentation
  • support vector machine

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