Publications (3)0 Total impact
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ABSTRACT: A new model for the correction of topographic effects in satellite images of rough terrain is described. The model simulates a synthetic image of the scene using a computer graphics approach which combines ray-tracing techniques with radiosity methods. Computation is structured on three levels: a macro level in which the image is described by the Digital Elevation Model and the light source, a meso-scale in which the model simulates the integration effect of the imaging sensor and a micro-scale which is characterized by the reflectance of the snow cover (specular and diffuse). The parameters of the model are tuned with a gradient search to fit real images acquired by the Landsat-TM sensor. The results show a better accuracy than the classical "cosine of incidence" and Minnaert models. Additionally a new technique based on maximum entropy estimation is used to determine the reflectance function of snow and compare it with the one predicted by our model.
07/1997;
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ABSTRACT: Estimations of the local roughness of Digital Elevation Models (DEMs) have been used both for a better geological understanding of terrain structures and for a more accurate DEM interpolation leading to higher resolution terrain maps. This work investigates two fractal dimension estimators for the characterization of the roughness of DEMs: the power spectrum estimator and the wavelet-based estimator. The performance of these methods is compared in terms of image segmentation capabilities. Since wavelets and fractals are related by the same multiresolution concept, we expect to have better results using the wavelet analysis method. This expectation is confirmed by experiments on synthetic images and real DEMs: measurements of fractal parameters using wavelet-based methods are more reliable than the same measurements performed using other methods. Finally we discuss some examples in which the fractal analysis of DEMs allows the separation of different roughness classes and reveals artifacts in the computation of elevation data.
07/1996;
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ABSTRACT: Hyperspectral, multispectral and multitemporal satellite images are considered as 3D signals and processed using 3D signal techniques. The third dimension is given by the spectral/temporal channels and the idea to perform a 3D processing is an attempt to take advantage of the correlation existing between these channels in order to achieve higher data compression rates. Two methods are investigated and compared: a 3D linear predictor based on a low order Markov model and a 3D wavelet decomposition procedure. Although the compression rate depends highly on the structure of the data, the 3D algorithms perform always better than the 2D ones. This justifies the use of 3D signal processing techniques in remote sensing applications involving a large number of correlated spectral or temporal channels.
04/1996;
