Getting pixels and regions to agree with conditional random fields

Land cover / land use classification of remotely sensed images is inherently geographical. The use of spatial information, accounting for neighborhood relationship and spatial smoothness of geographical objects, made its proofs in countless occasions and, especially when considering very high resolution images, methods ignoring spatial context do not perform well. In this paper, we propose a hybrid dual-layer conditional random field model that enforces spatial smoothness and consistency between the pixel and region-based maps. We formulate these intuitions as a standard energy minimization problem, and we show that finding a joint solution over both output spaces leads to strong improvements in the numerical and visual senses.