Image Classi†cation and Object Detection Using Spatial Contextual Constraints

Spatial information plays a fundamental role in the analysis and understanding of remotely sensed data sets. Common ways of incorporating spatial information into classi†cation involve the use of textural, morphological, and object-based features. Features extracted using co-occurrence matrices, Gabor wavelets [1], morphological pro†les [2], and Markov random †elds [3] have been widely used in the literature to model spatial information in neighborhoods of pixels. However, problems such as scale selection and the detailed content of high-resolution imagery make the applicability of traditional †xed window-based methods dif†cult for such data sets.