Geospatial Object Partition Based on Angular Second Moment Kernel

Improvements in Synthetic Aperture Radar (SAR) image collection has revealed the ability to semantically describe scene complexity and abundant details. It is difficult for the traditional pixel-based methods to partition geospatial objects. A practical geospatial object partition method based on angular second moment kernel (ASMK) for SAR image is proposed. Firstly, a new kernel termed ASMK is designed in order to obtain accurate classification results. Then, based on classification results, river and urban areas as typical geospatial objects are partitioned. In order to obtain urban border accurately, a likelihood function to evaluate the possibility that one pixel belongs to urban area is established. Results of experiments with high-resolution TerraSAR-X spotlight data of the urban of Rosenheim in Germany demonstrate that the proposed method's effectivity and accuracy in object partition.