Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth Observation Images

Kevin Alonso, Mihai Datcu
2015 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
We present an accelerated probabilistic learning concept and its prototype implementation for mining heterogeneous Earth observation images, e.g., multispectral images, synthetic aperture radar (SAR) images, image time series, or geographical information systems (GIS) maps. The system prototype combines, at pixel level, the unsupervised clustering results of different features, extracted from heterogeneous satellite images and geographical information resources, with user-defined semantic
more » ... tions in order to calculate the posterior probabilities that allow the final probabilistic searches. The system is able to learn different semantic labels based on a newly developed Bayesian networks algorithm and allows different probabilistic retrieval methods of all semantically related images with only a few user interactions. The new algorithm reduces the computational cost, overperforming existing conventional systems, under certain conditions, by several orders of magnitude. The achieved speed-up allows the introduction of new feature models improving the learning capabilities of knowledge-driven image information mining systems and opening them to Big Data environments. Index Terms-Active learning (AL), bag-of-words (BoW), Bayesian networks, Big Data, data fusion, geographical information systems (GIS), image mining. 1939-1404
doi:10.1109/jstars.2015.2435491 fatcat:s2e7ntyz75dvrmg22yqmmai7ya