Geospatial Data Mining for National Security: Land Cover Classification and Semantic Grouping

Land cover classification for the evaluation of land cover changes over certain areas or time periods is crucial for geospatial modeling, environmental crisis evaluation and urban open space planning. Remotely sensed images of various spatial and spectral resolutions make it possible to classify land covers on the level of pixels. Semantic meanings of large regions consisting of hundreds of thousands of pixels cannot be revealed by discrete and individual pixel classes, but can be derived by integrating various groups of pixels using ontologies. This paper combines data of different resolutions for pixel classification by support vector classifiers, and proposes an efficient algorithm to group pixels based on classes of neighboring pixels. The algorithm is linear in the number of pixels of the target area, and is scalable to very large regions. It also re-evaluates imprecise classifications according to neighboring classes for region level semantic interpretations. Experiments on advanced spaceborne thermal emission and reflection radiometer (ASTER) data of more than six million pixels show that the proposed approach achieves up to 99.8% cross validation accuracy and 89.25% test accuracy for pixel classification, and can effectively and efficiently group pixels to generate high level semantic concepts.