Characterizing land cover from X-band COSMO-SkyMed images by neural networks

The launch of last-generation satellites (COSMO-SkyMed and TerraSAR-X), equipped with X-band sensors acquiring images with a very high spatial resolution, has opened up new challenges in the field of SAR image processing for remote sensing applications. In this work, a set of Spotlight and Stripmap COSMO-Skymed images taken the Tor Vergata-Frascati test site was considered to investigate on the potential of this type of data in characterizing sub-urban areas by exploiting both amplitude and phase information contained in the radar return. In particular, this contribution deals with the development of a pixel based classification technique based on Multi-Layer Perceptron (MLP) Neural Networks (NN). The results have been compared with a land cover map of the same area, achieved by means of a different neural network algorithm exploiting the information carried by the eight bands of WorldView-2 satellite.