Landuse classification by fusion of optical and multitemporal SAR imagery

Abstract

Optical imagery such as high resolution panchromatic or multispectral data, and synthetic aperture radar (SAR) data show different information about the imaged objects, and have different advantages and disadvantages when used for object extraction or landuse classification. Multispectral optical image data is largely determined by the type of the material an object consists of. Panchromatic data which is often available with a higher resolution than multispectral data emphasizes geometric detail of the objects, e.g. the complex structure of anthropogenic objects such as road networks. In contrary to this, SAR data contain information about surface roughness and - to a lower degree - soil moisture. These different types of information are referring to completely different object qualities and are, therefore, largely uncorrelated which helps to reduce ambiguities in the results of object extraction. The main advantage of passive optical imagery with respect to SAR data is the lack of the speckle effect leading to images with a far better extractability of linear as well as areal objects. A major advantage of SAR is its all-weather capability which allows the acquisition of time series of imagery with exact acquisition dates under any climatic conditions. In this paper, these complementary properties of SAR and optical image data are demonstrated and used to improve landuse classification results.