Airborne line scanner measurements for ERS-1 SAR interpretation of sea ice

Abstract

A new method is presented for interpretation of Synthetic Aperture Radar (SAR) images recorded with the first European Remote Sensing Satellite (ERS-1) with respect to classification of sea ice. Conventional interpretation of spaceborne radar images is often based on subjective classification of radar signatures. Themost promising approach is a region-based segmentation with subsequent interpretation of the segments. The definition of regions with common properties enables a characterisation by texture parameters besides backscatter coefficients. The major drawback of this method is the subjective selection and characterisation of the training segments on which the classification is based. To avoid this subjective influence, an attempt is made of an independent classification by the use of two airborne line scanner systems, one for the visible and the other for the infrared spectral range. The systems were installed on research aircraft (aeroplane and helicopter) during an experiment covering the marginal ice zone north of Spitsbergen in late winter 1993 and in the North East Water Polynya in summer 1993. Several underflights of ERS-1 were carried out during the SAR mode. Typical areal coverage of the line scanner images during a flight is 1.5% of a SAR scene. The line scanner images are classified using a two-dimensional feature space. By this procedure various ice types are identified. After matching the locations of the line scanner and the segmented SAR images, it is then possible to derive the characteristics of different ice types with respect to radar signatures. Backscatter coefficient and texture parameters are used for the discrimination of ice types. Texture parameters used are moments of the grey-level distribution and values of the co-occurrence matrices. The results of this work show, that the common method of a subjective characterisation of training regions runs into difficulties. SAR signatures are essentially dependent on the conditions during ice development. Regions with different ice types can have the same SAR signatures and different SAR signatures may be found for the same ice type. With the presented method it is possible to achieve classification accuracies up to 78% with a discrimination of young ice, first-year ice and old ice during winter situations. In summer, it is difficult to discriminate wind exposed water and sea ice. In this case, the classification of SAR images is derived exclusively by means of texture parameters since the backscatter coefficient is of little use. With the discrimination of water, snow-covered ice, and brash ice an accuracy of 73% is achieved under summer conditions.