In this paper we propose a novel algorithm for the pansharpening of multispectral images based on the use of a Total Variation (TV) image prior. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images, and uses the sensor characteristics to model the observation process of both panchromatic and multispectral ...

Multispectral images are a particular class of images that require specialized coding algorithms. In multispectral images, the same spatial region is captured multiple times using different imaging modalities. These modalities often consist of measurements at different optical wavelengths (hence the name multispectral), but the same term is sometimes used when the separate image planes are capt...

Hyperspectral images have the high spectral resolution rather than to multispectral images. By development of remote sensing technology, the new sensors with hyperspectral capabilities in RS science will be replaced to multispectral imaging. A big advantage of hyperspectral images comparison to that of multispectral images is a continuous spectrum for each image cell that can be derived from im...

Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7 ETM+) provide low-spatial high-spectral resolution multispectral (MS) or high-spatial low-spectral resolution panchromatic (PAN) images, separately. In order to reconstruct a high-spatial/high-spectral resolution multispectral image volume, either the information in MS and PAN images are fused (i.e. pansharpening) or super...

In this paper we present a super resolution Bayesian methodology for pansharpening of multispectral images. By following the hierarchical Bayesian framework, and by applying variational methods to approximate probability distributions this methodology is able to: (a) incorporate prior knowledge on the expected characteristics of the multispectral images, (b) use the sensor characteristics to mo...

When performing post-classification comparison using images of different sensors, change detection is still possible even if images have different resolutions. However, in this case, change pixels are detected in the pixel size of coarser resolution image. This problem can be solved using higher resolution aerial photographs or panchromatic images if available. In multi sensor image fusion, ima...

We apply a general form of affine transformation model to compensate illumination variations in a series of multispectral images of a static scene and compare it to a particular affine and a diagonal transformation models. These models operate in the original multispectral space or in a lower-dimensional space obtained by Singular Value Decomposition (SVD) of the set of images. We use a system ...

In this theoretical paper we address the question of how to encode the local signal variation of multidimensional, multispectral signals. To this end, we first extend the concept of intrinsic dimension to the case of multispectral images in a way which is not depending upon the chosen colour space. We then show how additive, multiplicative, and occluded superpositions of oriented layers can be ...

A new super resolution Bayesian method for pansharpening multispectral images which incorporates sensor characteristics to model the observation process of both the panchromatic and the multispectral images is presented in this paper. The quality of the pansharpened images is assessed both qualitatively and quantitatively. Preliminary results are very promising: the method succeeded in preservi...

Image compression has been one of the mainstream research topics in image processing. The compression methods are usually developed for images visible to humans , i.e. for gray-scale or RGB images. Recent advances in the eld of remote sensing, however, require us to compress multispectral images. Many methods used in the traditional lossy image compression can be reused also in the compression ...