Image matching is a stage one performs as soon as one has two images of the same scene, taken from two different points of view. Matching these images aims at finding the mathematical transformation that enables passing from any point of the first one to the corresponding point in the other. As this study is related to satellite images, we show that the geometrical transformation can be approximated by a homography. Furthermore we want to match two clusters of points with no information of radiometry. Therefore, we have to guess the right parameters for this homography, by minimizing an appropriate cost function we define here. Then, the topography of the cost function is our main concern for the minimisation process. If looking for the right mathematical parameters seems the most natural way, we show that in this case the cost function has ''chaotic'' variations, so we need a complex technique for the minimization. To avoid this, we suggest guessing the parameters determining the conditions of the snapshot. Thus, we give the expression of the homography from these ''physical parameters'' and show that the topography of the cost function gets smoother. Thus the minimization process gets simpler.
In this paper, we use a multifractal approach based on the computation of two spectrums for image analysis and texture segmentation problems. The two spectrums are the Legendre Spectrum, determined by classical methods, and the Large Deviation Spectrum, determined by kernel density estimation. We propose a way for the fusion of these two spectrums to improve textured image segmentation results. An unsupervised k-means is used as clustering approach for the texture classification. The algorithm is applied on mosaic image built using IKONOS images and various natural textures from the Brodatz album. The segmentation obtained with our approach gives better results than the application of each spectrum separately.
We present in this paper a way to create transition classes and to represent them with vector structures. These classes are obtained using a supervised classification algorithm based on fuzzy decision trees. This method is useful to classify data which have a space evolution following a gradient such as forest, where transitions are spread over hundreds of meter, or other natural phenomenon. The vector representation is well adapted for integration in Geographical Information Systems because it is a more flexible structure than the raster representation. The method detailed takes into account local environmental conditions and leads to non regular gradient and fuzzy structures. It allows adding classes, called transition classes, when transition areas are too spread instead of fixing an arbitrary border between classes.
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