Geospatial intelligence about urban areas using SAR

摘要

Radar satellites are important for geospatial intelligence about urban areas and urban situational awareness, since these satellites can collect data at day and night and independently of weather conditions ensuring that the information can be obtained at regular intervals and in time. For this purpose we have applied change detection techniques developed at TNO to Radarsat I fine beam imagery of various dates to find changes in Baghdad during and after the war in 2003. A drawback of SAR imagery is the poor ability to recognize the detected changes in the scene. In this paper we present a workflow for the characterization and classification of changes detected in SAR imagery. We show that these changes can be characterized using complementary data and context information. For this purpose we have used a digital surface model from Ikonos stereo imagery that contains building heights. We also have used so-called temporal features extracted from a multi-temporal data-set of Radarsat data to select the changes and to detect activity between 2003 and 2007, which has been classified with high-resolution optical data.