Fusion of multisensor remote sensing data for urban land cover classification

The motivation for data fusion is to reduce the limitations and uncertainties associated with single sensor data. In the context of remotely sensed data this is often performed by combining images of high spatial, resolution with those of high spectral resolution at different levels. In contrast to pixel-based approaches like Intensity-Hue-Saturation (IHS) or Principal Component (PC) we will focus on image fusion at feature level. The research of this paper was conducted within within the "HyScan" project which goal is to develop a GIS based analysis and mapping of surface characteristics in urban areas using hyperspectral images in combination with remote sensing data of very high spatial resolution. In most cases the classification of hyperspectral data is performed using methods like Spectral Angle Mapper (SAM) or Mixture Tuned Matched Filtering (MTMF). Reference spectra for those algorithms are stored in libraries which contain the spectra of pure materials so called endmembers. The problem is that endmembers that represent urban surface types often display a mixture of spectral pure materials and thus show flat spectra. As a result, those thematic endmembers can hardly be detected by standard algorithms like the Pixel Purity Index (PPI). As a consequence standard classification procedures fail. In order to improve the quality of results, we fuse hyperspectral data recorded by the DAIS sensor with high spatial resolution imagery (e.g. HRSC) for a combined endmember selection, classification, and structural analysis. After segmentation of the high spatial resolution data, appropriate thematic classes are manually defined. The resulting segments are used to detect a set of pure pixels in the hyperspectral data which represent thematic endmembers. The segments resulting from the spatial high resolution data are processed using the endmember abundances of the hyperspectral data through a combined classification. Method and initial results of our fusion method are presented for endmember selection and classification of urban surface characteristics.