Optics and Radar Image Fusion

Kurzfassung

Optical and Radar images cover two distinct aspects in the satellite image analysis. Optical images giving more semantic information derived e.g. from multispectral data, while the radar being more versatile due to independence on cloudy and night scenes. Due to the different detection methods can expect that the fusion of these different data types can lead to improvements in the overall information from the observed scene. We define, that the information content (IC) of a set of (multispectral) images can be optimally derived from the data, if spatial and spectral resolution is adequate to the task that has to be solved. Furthermore the information is masked by typical sensor smear and noise. Thus the information, which can be derived from remote sensing imagery, depends on the system performance or image quality (IQ). By the additional use of radar data (e.g. for classification), often no significant improvement in the result is visible. Thus, we expect a drastic improvement of IC in the fused image, if the IQ of the two sets is comparable. This can be also analyzed in terms of image quality (IQ) for the fused data. The main purpose of this contribution is to achieve a number representing IQ, as e.g. the National Image Interpretability Rating Scale (NIIRS). The chosen fusion algorithm was the Principal Component Analysis (PCA) applied and validate on different area sets in Germany.