Integrated Information Mining and Image Retrieval in Remote Sensing

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 information from large image databases. This chapter presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed multiand hyperspectral images 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 (SVM) 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 (OODB) with associated images stored in an image database. Interesting patterns are then retrieved using a query-by-example (QBE) approach. The evaluation of the study results using coverage and novelty measures validates the effectiveness of the information mining and image retrieval framework, which is potentially useful for applications such as agricultural and environmental monitoring.