UNSUPERVISED CHANGE DETECTION FOR URBAN EXPANSION MONITORING: AN OBJECT-BASED APPROACH

Change detection is by definition the capability to detect and highlight changes occurring in space and time. Earth Observation satellites represent a fundamental source of information thanks to repeatability in time and spatial resolution. In this paper, we propose an unsupervised change detection technique capable of processing a series of singledate built-up area extractions with two main goals: determining the age of different parts of an urban area and fixing errors due to the automatic extractions suggested in previous papers by our group. Results show a general stabilization of the Kappa value but further investigation is still necessary. The proposed algorithm is available to the general public as a part of a QGIS plugin named SENSUM Earth Observation (EO) tools.