S. De Backer

Noise-Resistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images

In this paper, a technique is presented for the fusion of multispectral (MS) and hyperspectral (HS) images to enhance the spatial resolution of the latter. The technique works in the wavelet domain and is based on a Bayesian estimation of the HS image, assuming a joint normal model for the images and an additive noise imaging model for the HS image. In the complete model, an operator is defined, describing the spatial degradation of the HS image. Since this operator is, in general, not exactly known and in order to alleviate the burden of solving the inverse operation (a deconvolution problem), an interpolation is performed a priori . Furthermore, the knowledge of the spatial degradation is restricted to an approximation based on the resolution difference between the images. The technique is compared to its counterpart in the image domain and validated for noisy conditions. Furthermore, its performance is compared to several state-of-the-art pansharpening techniques, in the case where the MS image becomes a panchromatic image, and to MS and HS image fusion techniques from the literature.

A band selection technique for spectral classification

In hyperspectral remote sensing, sensors acquire reflectance values at many different wavelength bands, to cover a complete spectral interval. These measurements are strongly correlated, and no new information might be added when increasing the spectral resolution. Moreover, the higher number of spectral bands increases the complexity of a classification task. Therefore, feature reduction is a crucial step. An alternative would be to choose the required sensor bands settings a priori. In this letter, we introduce a statistical procedure to provide band settings for a specific classification task. The proposed procedure selects wavelength band settings which optimize the separation between the different spectral classes. The method is applicable as a band reduction technique, but it can as well serve the purpose of data interpretation or be an aid in sensor design. Results on a vegetation classification task show an improvement in classification performance over feature selection and other band selection techniques.

Non-linear dimensionality reduction techniques for unsupervised feature extraction

Dimensionality reduction techniques have been regularly used for visualization of high-dimensional data sets. In this paper, reduction to d >= 2 is studied, with the purpose of feature extraction. Four different non-linear techniques are studied: multidimensional scaling, Sammons mapping, self-organizing maps and auto-associative feedforward networks. All four techniques will be presented in the same framework of optimization. A comparison with respect to feature extraction is made by evaluating the reduced feature sets ability to perform classification tasks. The experiments involve an artificial data set and grey-level and color texture data sets. We demonstrate the usefulness of non-linear techniques compared to linear feature extraction.

Wavelet Denoising of Multicomponent Images Using Gaussian Scale Mixture Models and a Noise-Free Image as Priors

In this paper, a Bayesian wavelet-based denoising procedure for multicomponent images is proposed. A denoising procedure is constructed that 1) fully accounts for the multicomponent image covariances, 2) makes use of Gaussian scale mixtures as prior models that approximate the marginal distributions of the wavelet coefficients well, and 3) makes use of a noise-free image as extra prior information. It is shown that such prior information is available with specific multicomponent image data of, e.g., remote sensing and biomedical imaging. Experiments are conducted in these two domains, in both simulated and real noisy conditions.

Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models

This contribution concerns the retrieval of colour texture. The interband correlation structure is considered by modeling the heavy-tailed image wavelet histograms with a multivariate generalized Gaussian. As a similarity measure we propose to use the Rao geodesic distance, which, in contrast to the Kullback-Leibler divergence, exists in a closed form for any fixed value of the shape pa rameter of the distribution. We apply this in several retrieval experiments. The modeling of the interband correlation significantly increases retrieval rates, while the geodesic distance is shown to outperform the Kullback- Leibler divergence. A multivariate Laplace distribution yields better results than a Gaussian, indicating the potential of a model with variable shape parameter together with the geodesic distance.

Multiscale white matter fiber tract coregistration: a new feature-based approach to align diffusion tensor data.

In this paper an automatic multiscale feature‐based rigid‐body coregistration technique for diffusion tensor imaging (DTI) based on the local curvature κ and torsion τ of the white matter (WM) fiber pathways is presented. As a similarity measure, the mean squared difference (MSD) of corresponding fiber pathways in (κ, τ)‐space is chosen. After the MSD is minimized along the arc length of the curve, principal component analysis is applied to calculate the transformation parameters. In addition, a scale‐space representation of the space curves is incorporated, resulting in a multiscale robust coregistration technique. This fully automatic technique inherently allows one to apply region of interest (ROI) coregistration, and is adequate for performing both global and local transformations. Simulations were performed on synthetic DT data to evaluate the coregistration accuracy and precision. An in vivo coregistration example is presented and compared with a voxel‐based coregistration approach, demonstrating the feasibility and advantages of the proposed technique to align DT data of the human brain. Magn Reson Med, 2006.

Generic wavelet-based hyperspectral classification applied to vegetation stress detection

This communication studies the detection of vegetation stress in hyperspectral data. Compared to traditional vegetation stress indices, the proposed approach uses the complete reflectance spectrum and its wavelet representation. The detection strategy is formulated as a classification problem. Experiments are conducted on fruit tree stress detection. The experiments show the superior performance of the proposed strategy and demonstrate its generic nature.

Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery

This paper presents the results of estimation of leaf chlorophyll concentration through model inversion, from hyperspectral imagery of artificially treated orchard crops. The objectives were to examine model inversion robustness under changing viewing conditions, and the potential of multi‐angle hyperspectral data to improve accuracy of chlorophyll estimation. The results were compared with leaf chlorophyll measurements from laboratory analysis and field spectroscopy. Two state‐of‐the‐art canopy models were compared. The first is a turbid medium canopy reflectance model (MCRM) and the second is a 3D model (FLIGHT). Both were linked to the PROSPECT leaf model. A linear regression using a single band was also performed as a reference. The different techniques were able to detect nutrient deficiencies that caused stress from the hyperspectral data obtained from the airborne AHS sensor. However, quantitative chlorophyll retrieval was found largely dependent on viewing conditions for regression and the turbid medium model inversion. In contrast, the 3D model was successful for all observations. It offers a robust technique to extract chlorophyll quantitatively from airborne hyperspectral data. When multi‐angular data were combined, the results for both the turbid medium and 3D model increased. Final RMSE values of 5.8 µg cm−2 (MCRM) and 4.7 µg cm−2 (FLIGHT) were obtained for chlorophyll retrieval on canopy level.

Wavelet-Based EM Algorithm for Multispectral-Image Restoration

In this paper, we present a technique for the restoration of multispectral images. The presented procedure is based on an expectation-maximization (EM) algorithm, which applies iteratively a deconvolution and a denoising step. The restoration is performed in a multispectral way instead of band-by-band. The deconvolution technique is a generalization of the EM-based grayscale-image restoration and allows for the reconstruction of spatial as well as spectral blurring. The denoising step is performed in wavelet domain. To account for interband correlations, a multispectral probability density model for the wavelet coefficients is chosen. Rather than using a multinormal model, we opted for a Gaussian scale mixture model, which is a heavy-tailed model. Also in this paper, the framework is extended to include an auxiliary image of the same scene to improve the restoration. Experiments on Landsat and AVIRIS multispectral remote-sensing images are conducted.

Texture segmentation by frequency-sensitive elliptical competitive learning

Abstract In this paper, a new learning algorithm is proposed with the purpose of texture segmentation. The algorithm is a competitive clustering scheme with two specific features: elliptical clustering is accomplished by incorporating the Mahalanobis distance measure into the learning rules and under-utilization of smaller clusters is avoided by incorporating a frequency-sensitive term. In the paper, an efficient learning rule that incorporates these features is elaborated. In the experimental section, several experiments demonstrate the usefulness of the proposed technique for the segmentation of textured images. On the compositions of textured images, Gabor filters were applied to generate texture features. The segmentation performance is compared to k -means clustering with and without the use of the Mahalanobis distance and to the ordinary competitive learning scheme. It is demonstrated that the proposed algorithm outperforms the others. A fuzzy version of the technique is introduced, and experimentally compared with fuzzy versions of the k- means and competitive clustering algorithms. The same conclusions as for the hard clustering case hold.