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A critical step in object-oriented geospatial analysis (OBIA) is image segmentation. Segments determined from a lower-spatial resolution image can be used as the context to analyse a corresponding image at a higher-spatial resolution. Due to inherent differences in perceptions of a scene at different spatial resolutions and co-registration, segment boundaries from the low spatial resolution image need to be adjusted before being applied to the high-spatial resolution image. This is a non-trivial task due to considerations such as noise, image complexity, and determining appropriate boundaries, etc. An innovative method was developed in the study to solve this. Adjustments were executed for each boundary pixel based on the minimization of an energy function characterizing local homogeneity. It executed adjustments based on a structure which rewarded movement towards edges, and superior changes towards homogeneity. The developed method was tested on a set of Quickbird, ASTER and a lower resolution, resampled, Quickbird image, over a study area in Ontario, Canada. Results showed that the adjusted-segment boundaries obtained from the lower resolution imagery aligned well with the features in the Quickbird imagery

An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery