Monitoring urban changes based on scale-space filtering and object-oriented classification

This paper introduces a multi-temporal image processing framework towards an efficient and (semi-) automated detection of urban changes. Nonlinear scale space filtering was embedded in an object-based classification procedure and the resulted simplified images provided a more compact and reliable source in order to generate image objects in various scales. In this manner the multiresolution segmentation outcome was constrained qualitatively. Multivariate alteration detection (MAD) transformation was applied afterwards on the simplified data to facilitate the detection of possible changes. The altered image regions along with the simplified data were further analyzed through a multilevel knowledge-based classification scheme. The developed algorithm was implemented on a number of multi-temporal data acquired by different remote sensing sensors. The qualitative and quantitative evaluation of change detection results performed with the help of the appropriate ancillary ground truth data. Experimental results demonstrated the effectiveness of the developed scale-space, object-oriented classification framework.

► Morphological scale space filtering contributed to a fine segmentation. ► The simplified data resulted to a more robust, simple and fast rule set structure. ► MAD transformation detected automatically the majority of urban changes. ► Scale-space, object-based analysis framework improved the classification result.