Magnus O. Ulfarsson

Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields

Hyperspectral remote sensing technology allows one to acquire a sequence of possibly hundreds of contiguous spectral images from ultraviolet to infrared. Conventional spectral classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependences, which leads to a dramatic decrease in classification accuracies. In this paper, a new automatic framework for the classification of hyperspectral images is proposed. The new method is based on combining hidden Markov random field segmentation with support vector machine (SVM) classifier. In order to preserve edges in the final classification map, a gradient step is taken into account. Experiments confirm that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies compared to the standard SVM method and also outperforms other studied methods.

Relationship between the spectral characteristics of atrial fibrillation and atrial tachycardias that occur after catheter ablation of atrial fibrillation

BACKGROUND During catheter ablation of complex fractionated atrial electrograms, persistent atrial fibrillation (AF) may convert to an atrial tachycardia (AT). OBJECTIVE The purpose of this study was to investigate the possible mechanisms of AT by examining the spectral and electrophysiologic characteristics of AF and ATs that occur after catheter ablation of AF. METHODS The subjects of this study were 33 consecutive patients with persistent AF who had conversion of AF to AT during ablation of AF (group I) and 20 consecutive patients who underwent ablation of persistent AT that developed more than 1 month after AF ablation (group II). Spectral analysis of the coronary sinus (CS) electrograms and lead V(1) was performed during AF at baseline, before conversion, and during AT. The spatial relationship between the AT mechanism and ablation sites was examined. RESULTS A spectral component with a frequency that matched the frequency of AT was present in the baseline periodogram of AF more often in group I (52%) than in group II (20%, P = .02). Ablation resulted in a decrease in the dominant frequency of AF but not in the frequency of the spectral component that matched the AT. There was a significant direct relationship between the baseline dominant frequency of AF and the frequency of AT in the CS in group I (r = 0.76, P <.0001) but not in group II (r = 0.38, P = .09). ATs were macroreentrant in 64% and 60% of patients in groups I and II, respectively (P = .8). The AT site was more likely to be distant (>1 cm) from AF ablation sites in group I (70%) than in group II (35%, P = .007). CONCLUSION The findings of this study suggest that ATs observed during ablation of AF often may be drivers of AF that become manifest after elimination of higher-frequency sources and fibrillatory conduction.

A New Pansharpening Algorithm Based on Total Variation

In this letter, we present a new method for the pansharpening of multispectral satellite imagery. Pansharpening is the process of synthesizing a high spatial resolution multispectral image from a low spatial resolution multispectral image and a high-resolution panchromatic (PAN) image. The method uses total variation to regularize an ill-posed problem dictated by a widely used explicit image formation model. This model is based on the assumptions that a linear combination of the bands of the pansharpened image gives the PAN image and that a decimation of the pansharpened image gives the original multispectral image. Experimental results are based on two real datasets and the quantitative quality of the pansharpened images is evaluated using a number of spatial and spectral metrics, some of which have been recently proposed and do not need a reference image. The proposed method compares favorably to other well-known methods for pansharpening and produces images of excellent spatial and spectral quality.

Model-Based Fusion of Multi- and Hyperspectral Images Using PCA and Wavelets

In remote sensing, due to cost and complexity issues, multispectral (MS) and hyperspectral (HS) sensors have significantly lower spatial resolution than panchromatic (PAN) images. Recently, the problem of fusing coregistered MS and HS images has gained some attention. In this paper, we propose a novel method for fusion of MS/HS and PAN images and of MS and HS images. MS and, more so, HS images contain spectral redundancy, which makes the dimensionality reduction of the data via principal component (PC) analysis very effective. The fusion is performed in the lower dimensional PC subspace; thus, we only need to estimate the first few PCs, instead of every spectral reflectance band, and without compromising the spectral and spatial quality. The benefits of the approach are substantially lower computational requirements and very high tolerance to noise in the observed data. Examples are presented using WorldView 2 data and a simulated data set based on a real HS image, with and without added noise.

Left atrial pressure and dominant frequency of atrial fibrillation in humans

BACKGROUND Atrial stretch is thought to play a role in the development of atrial fibrillation (AF). However, the precise mechanism by which stretch contributes to AF maintenance in humans is unknown. OBJECTIVE The purpose of this study was to determine the impact of left atrial (LA) pressure on AF frequency in patients undergoing catheter ablation of AF. METHODS The subjects of this study were 58 consecutive patients with persistent AF (n = 40) or paroxysmal AF (n = 18) undergoing LA ablation. LA pressure was measured before ablation. Both atria and the coronary sinus were mapped, and regional dominant frequency (DF) was determined. RESULTS Mean LA pressure in the persistent AF group was significantly higher than in the paroxysmal AF group (18 ± 5 vs 10 ± 4 mmHg, P <.0001). Mean DF in the persistent AF group was also higher than in the paroxysmal AF group (6.36 ± 0.51 Hz and 5.83 ± 0.54 Hz, P = .0006). In patients with persistent AF, there was a significant correlation between LA pressure and DF at the LA appendage (r = 0.55, P = .0002). DF(max) was found at the LA appendage region in 24 (60%) of the 40 patients with persistent AF (P = .0006). In multivariate analysis, LA pressure was the only independent predictor of DF(max) in the LA appendage (P = .04, odds ratio 1.41, 95% confidence interval 1.02-1.94). CONCLUSION Higher LA pressure in patients with persistent AF implies that these patients are more vulnerable to stretch-related remodeling than are patients with paroxysmal AF. The DF of AF was directly related to LA pressure in patients with persistent AF. This finding suggests that atrial stretch may contribute to the maintenance of AF in humans by stabilizing high-frequency sources.

Dimension Estimation in Noisy PCA With SURE and Random Matrix Theory

Principal component analysis (PCA) is one of the best known methods for dimensionality reduction. Perhaps the most important problem in using PCA is to determine the number of principal components (PCs) or equivalently choose the rank of the loading matrix. Many methods have been proposed to deal with this problem but almost all of them fail in the important practical case when the number of observation is comparable to the number of variables, i.e., the realm of random matrix theory (RMT). In this paper we propose to use Steins unbiased risk estimator (SURE) to estimate, with some assistance from RMT, the number of principal components. The method is applied both on simulated and real functional magnetic resonance imaging (fMRI) data, and compared to BIC and the Laplace method.

Hyperspectral image denoising using 3D wavelets

In this paper, we propose a denoising method for hyperspectral images using 3D wavelets. We use the sparse analysis regularization using a 3D overcomplete wavelet dictionary. The minimization problem is solved using iterative Chambolle algorithm. The simulation results show that the 3D dictionary outperforms the 2D one, in terms of Peak Signal to Noise Ratio (PSNR). Denosing hysperspectral cubes is likely to increase the classification accuracy of the hyperspectral data since it can enhance the spectral profiles (or features) that can be useful to discriminate between information classes.

Speckle reduction of SAR images in the curvelet domain

Curvelet transform (CT), proposed by E. Candes et al. (1999), is used for speckle reduction of SAR images. The CT is useful for speckle reduction through its subband images and the speckle reduction is obtained by thresholding the subband-image coefficients of the digitized SAR images. Two thresholding methods are used; hard thresholding and soft thresholding. The denoising method shows great promise for speckle removal and hence provides good detection performance for SAR based recognition.

Hyperspectral Image Denoising Using First Order Spectral Roughness Penalty in Wavelet Domain

In this paper, a new denoising method for hyperspectral images is proposed using First Order Roughness Penalty (FORP). FORP is applied in the wavelet domain to exploit the Multi-Resolution Analysis (MRA) property of wavelets. Steins Unbiased Risk Estimator (SURE) is used to choose the tuning parameters automatically. The simulation results show that the penalized least squares using FORP can improve the Signal to Noise Ratio (SNR) compared to other denoising methods. The proposed method is also applied to a corrupted hyperspectral data set and it is shown that certain classification indices improve significantly.

Complex electrograms within the coronary sinus: time- and frequency-domain characteristics, effects of antral pulmonary vein isolation, and relationship to clinical outcome in patients with paroxysmal and persistent atrial fibrillation.

Background: The mechanistic and clinical significance of complex fractionated atrial electrograms (CFAE) in the coronary sinus (CS) has been unclear.