An mean shift algorithm with adaptive bandwidth and weight selection for high spatial remotely sensed imagery segmentation

Qinling Dai, Leiguang Wang, Qizhi Xu, Yun Zhang

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

Abstract

An improved mean shift segmentation method featuring adaptive parameter selection is presented in this paper. We associate the bandwidths and weight for each point in a spatial-range feature space with boundary information in an image plane. Varying weight and bandwidth for each pixel are assigned according to a boundary map, which is obtained by learning multiple edge cues. We consider two groups of edge cues and two regressing modules, approach the cue combination as a supervised learning problem from the ground truth data (manually sketched boundary maps). From our preliminary results, the provided method can combine the top-down information got from regression models with the mean shift process and constrain over-clustering of pixels belonging different land objects.

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1592-1595
Number of pages4
ISBN (Electronic)9781479957750
DOIs
Publication statusPublished - 4 Nov 2014
Externally publishedYes
EventJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada
Duration: 13 Jul 201418 Jul 2014

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

ConferenceJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Country/TerritoryCanada
CityQuebec City
Period13/07/1418/07/14

Keywords

  • Mean shift segmentation
  • edge detector
  • regression model

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