An agglomerative hierarchical clustering based high-resolution remote sensing image segmentation algorithm

Rongjie Liu*, Jie Zhang, Pingjian Song, Fengjing Shao, Guanfeng Liu

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Remote sensing image segmentation is the basis of image pattern recognition. It is significant for the application and analysis of remote sensing images. Clustering analysis as a non-supervised learning method is widely used in the segmentation of remote sensing images. It has made good results in the segmentation of low-resolution and moderate-resolution remote sensing images. As the improvement of image resolution, however, they have problems in the segmentation of high-resolution remote sensing images. In this paper we propose an Agglomerative Hierarchical Clustering based High-Resolution Remote Sensing Image Segmentation Algorithm. The segmentation experiments show that the result of this algorithm is better than the K-Means' and is close to the results of artificial extraction.

Original languageEnglish
Title of host publicationProceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
Pages403-406
Number of pages4
Volume4
DOIs
Publication statusPublished - 2008
EventInternational Conference on Computer Science and Software Engineering, CSSE 2008 - Wuhan, Hubei, China
Duration: 12 Dec 200814 Dec 2008

Other

OtherInternational Conference on Computer Science and Software Engineering, CSSE 2008
CountryChina
CityWuhan, Hubei
Period12/12/0814/12/08

Keywords

  • Agglomerative hierarchical clustering method
  • High-resolution remote sensing image segmentation
  • Remote sensing

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