This paper presents a novel method for the enhancement of spatial quality of Hyperspectral (HS) images while making use of a high resolution panchromatic (PAN) image. Due to the high number of bands the application of a pansharpening technique to HS images may result in an increase of the computational load and complexity. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. To solve this problem we propose a pansharpening technique combining both dimensionality reduction and fusion, exploited by non-linear Principal Component Analysis (NLPCA) and Indusion respectively, to enhance the spatial resolution of a hyperspectral image.

The fusion of multispectral (MS) and panchromatic (PAN) images is a useful technique for enhancing the spatial quality of low-resolution MS images. Liu recently proposed the smoothing-filter-based intensity modulation (SFIM) fusion technique. This technique upscales MS images using bicubic interpolation and introduces high-frequency information of the PAN image into the MS images. However, this fusion technique is plagued by blurred edges if the upscaled MS images are not accurately coregistered with the PAN image. In the first part of this letter, we propose the use of the Induction scaling technique instead of bicubic interpolation to obtain sharper, better correlated, and hence better coregistered upscaled images. In the second part, we propose a new fusion technique derived from induction, which is named ldquoIndusion.rdquo In this method, the high-frequency content of the PAN image is extracted using a pair of upscaling and downscaling filters. It is then added to an upscaled MS image. Finally, a comparison of SFIM (with both bicubic interpolation and induction scaling) is presented along with the fusion results obtained by IHS, discrete wavelet transform, and the proposed Indusion techniques using Quickbird satellite images.

Pan-sharpening is the process of improving spatial resolution of multi-spectral (MS) satellite images using the spatial details of a high resolution Panchromatic (PAN) image. Pan sharpening can be divided into scaling and fusion processes. In the first part of this paper we use Induction instead of bicubic interpolation for up-scaling the MS images. SFIM (Smoothing Filter based Intensity Modulation) is used to obtain fused MS images for the two different scaling techniques. In the second part, “Indusion”, a new fusion technique, derived from Induction, is proposed. In this technique the high frequency content of the PAN image is extracted using a pair of up-scaling and down scaling filters. It is then added to the up-scaled MS images. Finally a comparison of Indusion with Intensity, Hue, Saturation (HIS), Discrete Wavelet Transform (DWT) and SFIM fusion techniques is presented for IKONOS satellite images.

In binary images, the Distance Transformation (DT) and the geometrical skeleton extraction are classic tools for shape analysis. In this paper, we present time optimal algorithms to solve the reverse Euclidean distance transformation and the reversible medial axis extraction problems for d-dimensional images. We also present a d-dimensional medial axis filtering process that allows us to control the quality of the reconstructed shape.

An original image magnification method called induction was proposed recently, whose specificity is to state the problem of image magnification as an inverse problem of image reduction. The methods usually employed, like interpolation, fail to verify this constraint, which provides a formalism for magnification and a framework for evaluating the quality of the enlarged images. In this paper, we revisit the induction through a new interpretation using wavelets. We put forward major improvements including a direct implementation, much more efficient than the iterative algorithm proposed previously.

This work highlights the relation existing between isosurfaces of an image with a given threshold (as classically computed by a marching-cubes algorithm) and digital surfaces of this thresholded image. The first step has been to extend the classical adjacency relation defined between elements of a digital surface. It turns out that the induced surface graphs are 2D combinatorial manifolds without boundary which can easily be mapped into closed and orientable surfaces in R/sup 3/. Hence, digital surfaces can be processed to compute corresponding isosurfaces; the converse is also true.

This article presents a general scheme for contour deformation analysis. It is based on a point-to-point multiresolution matching and an original representation of deformation. After a short presentation, we describe how this model allows us to detect and measure motion of articulated objects without any a priori knowledge. Then we show how a frequency analysis of our deformation representation is directly connected to a meaningful distinction between local and global deformations. It allows us to perform deformation analysis and deformation filtering. Filtering allows one to synthesize a contour corresponding to only a selection of the components of an observed deformation.

The skeleton of an object is the locus of the centers of maximal discs included in the shape. The skeleton provides a compact representation of objects, useful for shape description and recognition. A well-known drawback of the skeleton transformation is its lack of continuity. This paper is concerned with the modeling of noise that may affect objects and the consequence of this noise on the skeleton. A graph (called the parameter graph) is introduced, on which branches due to noise are characterized. We deduce from this preliminary study a method to simplify skeletons. It depends on thresholds that can be chosen directly on the parameter graph associated to each skeleton.

Segmentation in regions may be achieved using pyramidal methods that provide hierarchical partitions of the original image. The irregular pyramid is a stack of adjacency graphs of the regions in which the shape of the regions is content based. Using the hierarchical decomposition of an image given by this pyramid, a multiresolution oriented process is proposed. Many segmentations at different resolutions are obtained by changing the value of a scale parameter. Thus, each entity may be extracted from the pyramid at its best resolution.

This paper presents an approach to approximate the skeleton of continuous shapes either in 2D or 3D space. The data required is a sampling of the boundary of the shape. The authors call polyball any finite union of balls. A preliminary work on polyballs shows that their skeletons consist of simple components (line segments in 2D and polygons in 3D). To construct these components, only the computation of a Voronoi graph is required. Previous papers have proposed to approximate the skeleton of continuous shapes using the Voronoi graph of boundary points. An original reformulation of these methods is presented here, using polyballs. It allows one to build a hierarchy of simplified skeletons. An application in the frame of a European project in the field of medicine and biology is also presented. The skeleton by influence zones is computed in real time, which validates the authors' approach.

Image mosaicking consists in fusing images acquired from different places to build a global view of a scene. In this paper we present a method to mosaic images lying on three-dimensional surfaces. A rough model of the surface and the parameters of the projection permit images to be flattened. Then images are merged with a 2D technique of mosaicking. We propose to apply corrections on original images or on Laplacian images. The resulting image is mapped on an approximated elevation model to allow 3D visualization. Such an approach allows to take into account the perspective distortions and to reduce discontinuities along the junction line. This method is available for various domains (microscopy, paintings, satellite, etc.). Here we illustrate our method with paintings on a vault.

The chamfer distances are based on the definition of masks whose size can change depending on the quality of the approximation which is expected, compared to the Euclidean distance. The authors show the induced geometrical properties of the generated distance images, and calculate the required properties of the mask to ensure that they define a distance function. Then they show how to optimize the masks directly in discrete space, and finally, present some main applications.>

A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. Like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(image-size) steps. However, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. Two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. In labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. In gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes.>

Independent stochastic processes are used to generate in parallel irregular tessellations of an image. Recursive application of the procedure yields a multiresolution hierarchy adapted to the content of the image. The hierarchy is similar to traditional image pyramids: its height is of log (image size) order, but by being adapted to the input, it eliminates the artifacts frequently present in the latter. The output obtained at the apex of the hierarchy can be used for fast image analysis. The technique was applied to connected component labeling (labeled images) and segmentation (gray-level images).>