Strategies for Integrating Information from Multiple Spatial Resolutions into Land-Use/Land-Cover Classification Routines

With the development of new remote sensing systems, veryhigh spatial and spectral resolution images now provide a source for detailed and continuous sampling of the Earth’s surface from local to regional scales. This paper presents three strategies for selecting and integrating information from different spatial resolutions into classification routines. One strategy is to combine layers of images of varying resolution. A second strategy involves comparing the a posteriori probabilities of each class at different resolutions. Another strategy is based on a top-down approach starting with the coarsest resolution. The multiresolution strategies are tested using simulated multiresolution images for a portion of the rural-urban fringe of the San Diego Metropolitan Area. The classification accuracy obtained from using three multiple strategies was greater when compared with that from a conventional single-resolution approach. Among the three strategies, the top-down approach resulted in the highest classification accuracy with a Kappa value of 0.648, compared to a Kappa of 0.566 for the conventional classifier.