Land-use mapping using coarse resolution SAR data at the object level exploiting ancillary optical data

The work presented in this paper is devoted to the analysis of SAR images in order to produce at first a human settlements map, followed by a refined classification of the same dataset in order to extract a Land Use Land Cover (LU/LC) map based on CORINE nomenclature. The urban extents are computed using an approach based on Local Indicators of Spatial Association and textural features while the LU/LC map is obtained using a segmentation technique in order to exploit the statical behaviours of areas belonging to the same class. In particular, in this paper the joint use of SAR (for classification) and optical images (for segmentation) on the same area is investigated, together with the comparison of three different segmentation techniques based on different algorithms and approaches. Conducted tests on the area of Shanghai demonstrated that the use of even dated optical images for the segmentation phase increases the accuracy and reveals the potential of the entire processing chain for urban areas monitoring.