Learning - unlearning for mining high resolution EO images

The last two decades showed an important development of satellite imagery with past and present satellites acquiring enormous volumes of data. Meanwhile, the quality of the acquired images increased permitting the recording of high resolution images (0.6divide2.5 meters/pixel) in multispectral bands. Thus, both the data volume and the information detail increase dramatically. Consequently, new methods and tools to access and interpret earth observation (EO) images are needed. The present paper presents a semantic search engine for high resolution (HR) EO images based on a hierarchical information model of satellite image contents. To face the potentially ambiguous meaning of image structures depending on their contextual understanding, the search engine uses Bayesian inference to learn categories and a support vector machine (SVM) classifier to assign semantics. The categories are grouping and memorising the semantics of image structures, facilitating their recognition in various contexts. Also the generation of categories helps learning from a small training data set (i.e. image examples); thus, the method is useful for the exploitation of very large data volumes. The concept has enhanced inferred power, therefore optimising the human machine communication (HMC), which is enhanced with learning / unlearning functions.