Integrated spectral and spatial information mining in remote sensing imagery

Research output: Contribution to journalArticle

96 Citations (Scopus)

Abstract

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
Volume42
Issue number3
DOIs
StatePublished - Mar 1 2004

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Remote sensing
imagery
remote sensing
Image retrieval
Support vector machines
Data mining
land cover
Monitoring
data mining
Sensors
image classification
environmental monitoring
integrated approach
wavelet
sensor
Object-oriented databases

All Science Journal Classification (ASJC) codes

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

Cite this

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title = "Integrated spectral and spatial information mining in remote sensing imagery",
abstract = "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.",
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Integrated spectral and spatial information mining in remote sensing imagery. / Li, Jiang; Narayanan, Ram M.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 3, 01.03.2004, p. 673-685.

Research output: Contribution to journalArticle

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