TY - JOUR
T1 - Integrated spectral and spatial information mining in remote sensing imagery
AU - Li, Jiang
AU - Narayanan, Ram M.
N1 - Funding Information:
Manuscript received August 25, 2003; revised December 11, 2003. This project was supported by the Nebraska Research Initiative Geospatial Data Analysis project. J. Li is with the Department of Computer Science and Information Technology, Austin Peay State University, Clarksville, TN 37044 USA. R. M. Narayanan is with the Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802 USA (e-mail: ram@ee.psu.edu). Digital Object Identifier 10.1109/TGRS.2004.824221
PY - 2004/3
Y1 - 2004/3
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.
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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 -