Integrated spectral and spatial information mining in remote sensing imagery

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105 Scopus citations


Most existing remote sensing image retrieval systems allow only simple queries based on sensor, location, and date of image capture. This approach does not permit the efficient retrieval of useful hidden information from large image databases. This paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies. Land cover information corresponding to spectral characteristics is identified by supervised classification based on support vector machines with automatic model selection, while textural features characterizing spatial information are extracted using Gabor wavelet coefficients. Within identified land cover categories, textural features are clustered to acquire search-efficient space in an object-oriented database with associated images in an image database. Interesting patterns are then retrieved using a query-by-example approach. The evaluation of the study results using coverage and novelty measures validates the effectiveness of the proposed remote sensing image information mining framework, which is potentially useful for applications such as agricultural and environmental monitoring.

Original languageEnglish (US)
Pages (from-to)673-685
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number3
StatePublished - Mar 2004

All Science Journal Classification (ASJC) codes

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


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