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 language | English (US) |
|---|---|
| Pages (from-to) | 673-685 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Geoscience and Remote Sensing |
| Volume | 42 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 1 2004 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)
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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 journal › Article
TY - JOUR
T1 - Integrated spectral and spatial information mining in remote sensing imagery
AU - Li, Jiang
AU - Narayanan, Ram M.
PY - 2004/3/1
Y1 - 2004/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=1842429086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=1842429086&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2004.824221
DO - 10.1109/TGRS.2004.824221
M3 - Article
AN - SCOPUS:1842429086
VL - 42
SP - 673
EP - 685
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
IS - 3
ER -