An information fusion method for multispectral image classification postprocessing

Remote-sensing image classification is one of the most important techniques in understanding the dynamics of the Earth´s ecosystems. Various approaches have been proposed for performing this classification task. Obtained classification results are generally shown as a thematic (or class) map in which each pixel is assigned a class label. Due to sensor noise and algorithm limitations, obtained thematic maps are very noisy. The noise has a “salt-and-pepper” appearance in homogeneous regions and produces weakly defined interregion borders. In this paper, a new postprocessing approach aiming to produce thematic maps with sharp interregion boundaries and homogeneous regions is presented. This approach is conducted in two steps: (1) relevant features derived from the original multispectral image (edge maps) as well as from the thematic map, the Smoothed Thematic Map (STM), are determined and (2) a region-growing algorithm is applied over the thematic map. This algorithm grows until reaching an edge (from the edge maps) or a class change in the STM. The proposed approach fills the requirements of being independent of the used classification algorithm and not knowledge-based (in the sense that no a priori information concerning the contents of the considered image is needed). Tests have been conducted on a Landsat image covering mainly agricultural areas