Integrated spectral and spatial information mining in remote sensing imagery

• Published in: IEEE transactions on geoscience and remote sensing 2004-03, Vol.42 (3), p.673-685
• Main Authors: Jiang Li, Narayanan, R.M.
• Format: Article
• Language: English
• Subjects: Applied geophysics; Data mining; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geochemical exploration, methodology, general; Image databases; Image retrieval; Image sensors; Information retrieval; Integrated approach; Internal geophysics; Metallic and non-metallic deposits; Object oriented databases; Remote monitoring; Remote sensing; Space technology; Spatial databases; Studies
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2004.824221

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.