Segmentation of satellite imagery of natural scenes using data mining

Leen Kiat Soh, Costas Tsatsoulis

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


In this paper we describe a segmentation technique that integrates traditional image processing algorithms with techniques adapted from knowledge discovery in databases (KDD) and data mining to analyze and segment unstructured satellite images of natural scenes. We have divided our segmentation task into three major steps. First an initial segmentation is achieved using dynamic local thresholding producing a set of regions. Then spectral spatial and textural features for each region are generated from the thresholded image. Finally given these features as attributes an unsupervised machine learning methodology called conceptual clustering is used to cluster the regions found in the image into N classes-thus determining the number of classes in the image automatically. We have applied the technique successfully to ERS-1 synthetic aperture radar (SAR) Landsat thematic mapper (TM) and NOAA advanced very high resolution radiometer (AVHRR) data of natural scenes.

Original languageEnglish (US)
Pages (from-to)1086-1099
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number2 II
StatePublished - 1999
Externally publishedYes


  • Clustering methods
  • Image segmentation
  • Natural scene analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


Dive into the research topics of 'Segmentation of satellite imagery of natural scenes using data mining'. Together they form a unique fingerprint.

Cite this