Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.

A Critical Comparison Among Pansharpening Algorithms

A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

Pansharpening by Convolutional Neural Networks

Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literatures for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state-of-the-art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Eleven methods from different classes (component substitution, multiresolution analysis, hybrid, Bayesian and matrix factorization) are analyzed. These methods are applied to three datasets and their effectiveness and robustness are evaluated with widely used performance indicators. In addition, all the pansharpening techniques considered in this paper have been implemented in a MATLAB toolbox that is made available to the community.

/pdf/hyperspectral-pansharpening-a-review-i5b1k92tih.pdf

Hyperspectral Pansharpening: A Review

Stabilization of panoramic videos from mobile multi-camera platforms

This paper presents a novel method for the spatial quality improvement of low resolution Hyperspectral (HS) images by making use of a high resolution panchromatic (Pan) image. Since the introduction of all the details extracted from the Pan image into the upscaled HS images may result in spectral and spatial distortions, a detail injection model based on the optimization of QNR spatial quality index is proposed. This proposed model produces pansharpened images while preserving spectral fidelity. Also, to speed up the fusion process we propose to use the Universal Image Quality Index (UIQI) for dimensionality reduction before performing pansharpening. Finally, a comparison of the proposed method is presented with some existing pansharpening methods.

/pdf/pansharpening-of-hyperspectral-images-using-spatial-2t1v49o062.pdf

Pansharpening of Hyperspectral images using spatial distortion optimization

Disclosed are a color correction apparatus for panorama video stitching and a method of selecting a reference image using the same. A method of selecting a reference image for color correction when stitching panorama video based on input images includes selecting an optimum reference image candidate from the input images based on standard deviations for overlapping regions between the input images, performing color correction on the input images based on the optimum reference image candidate, and validating the optimum reference image candidate based on the color-corrected input images.

Color correction apparatus for panorama video stitching and method for selecting reference image using the same

The problem with visualization of hyper-spectral images on tri-stimulus displays arises from the fact that they contain hundreds of spectral bands while generally used display devices support only three bands/channels namely blue, green and red. Therefore, for visualization a hyper-spectral (HS) image has to be reduced to three bands. The main challenge while performing this band reduction is to retain and display the maximum information available in a hyper-spectral image. Human visual system focuses attention on certain regions in images called “salient regions”. Therefore to provide a comprehensive representation of hyper-spectral data on tri-stimulus displays we propose to use a weighted fusion method of saliency maps and hyper-spectral bands. The efficacy of the proposed algorithm has been demonstrated by tests on both urban and countryside images of AVIRIS and ROSIS sensors.

/pdf/saliency-based-visualization-of-hyper-spectral-images-3cp4mu7cna.pdf

Saliency based visualization of hyper-spectral images

We present a novel methodology for the fusion of multiple (two or more) images using the multivariate extension of empirical mode decomposition (MEMD). Empirical mode decomposition (EMD) is a data-driven method which decomposes input data into its intrinsic oscillatory modes, known as intrinsic mode functions (IMFs), without making a priori assumptions regarding the data. We show that the multivariate and multidimensional extensions of EMD are suitable for image fusion purposes. We further demonstrate that while multidimensional extensions, by design, may seem more appropriate for tasks related to image processing, the proposed multivariate extension outperforms these in image fusion applications owing to its mode-alignment property for IMFs. Case studies involving multi-focus image fusion and pan-sharpening of multi-spectral images are presented to demonstrate the effectiveness of the proposed method.

/pdf/image-fusion-using-multivariate-and-multidimensional-emd-ivax8ue7i3.pdf

Muhammad Murtaza Khan

Papers

Stabilization of panoramic videos from mobile multi-camera platforms

Pansharpening of Hyperspectral images using spatial distortion optimization

Color correction apparatus for panorama video stitching and method for selecting reference image using the same

Saliency based visualization of hyper-spectral images

Image fusion using multivariate and multidimensional EMD