Behnood Rasti

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.

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.

Wavelet-Based Sparse Reduced-Rank Regression for Hyperspectral Image Restoration

In this paper, a method called wavelet-based sparse reduced-rank regression (WSRRR) is proposed for hyperspectral image restoration. The method is based on minimizing a sparse regularization problem subject to an orthogonality constraint. A cyclic descent-type algorithm is derived for solving the minimization problem. For selecting the tuning parameters, we propose a method based on Steins unbiased risk estimation. It is shown that the hyperspectral image can be restored using a few sparse components. The method is evaluated using signal-to-noise ratio and spectral angle distance for a simulated noisy data set and by classification accuracies for a real data set. Two different classifiers, namely, support vector machines and random forest, are used in this paper. The method is compared to other restoration methods, and it is shown that WSRRR outperforms them for the simulated noisy data set. It is also shown in the experiments on a real data set that WSRRR not only effectively removes noise but also maintains more fine features compared to other methods used. WSRRR also gives higher classification accuracies.

Hyperspectral image denoising using a new linear model and Sparse Regularization

This paper deals with hyperspectral image reconstruction using a new linear model and Sparse Regularization (SR). The new model is based on Principal Components (PCs) and wavelets. Since the hyperspectral PCs are not spatially sparse, wavelet is applied to get spatially sparse representation. Sparse regularization is used to recover the corrupted signal. The regularization parameter is chosen by Steins Unbiased Risk Estimator (SURE). The results show improvements for simulated data sets compare to other denoising methods based on Signal to Noise Ratio (SNR). In addition, the methods are applied on a real noisy data set, and the results of the new method demonstrate visual improvement. The proposed approach is automatic, fast and has the ability to be applied on very large data sets.

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information. They have made a plethora of applications feasible for the analysis of large areas of the Earth?s surface. However, a significant number of factors-such as the high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light-scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions-make such data inherently nonlinear and complex, which poses major challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.

Hyperspectral Feature Extraction Using Total Variation Component Analysis

In this paper, a novel feature extraction method, called orthogonal total variation component analysis (OTVCA), is proposed for remotely sensed hyperspectral data. The features are extracted by minimizing a total variation (TV) penalized optimization problem. The TV penalty promotes piecewise smoothness of the extracted features which is useful for classification. A cyclic descent algorithm called OTVCA-CD is proposed for solving the minimization problem. In the experiments, OTVCA is applied on a rural hyperspectral image having low spatial resolution and an urban hyperspectral image having high spatial resolution. The features extracted by OTVCA show considerable improvements in terms of classification accuracy compared with features extracted by other state-of-the-art methods.

Hyperspectral Subspace Identification Using SURE

The identification of the signal subspace is a very important first step for most hyperspectral algorithms. In this letter, we investigate the important problem of identifying the hyperspectral signal subspace by minimizing the mean squared error (MSE) between the true signal and an estimate of the signal. Since it is dependent on the true signal, the MSE is uncomputable in practice, and so we propose a method based on Steins unbiased risk estimator that provides an unbiased estimate of the MSE. The resulting method is simple and fully automatic, and we evaluate it using both simulated and real hyperspectral data sets. Experimental results show that our proposed method compares well to recent state-of-the-art subspace identification methods.

Total variation based hyperspectral feature extraction

In this paper, a hyperspectral feature extraction method is proposed. A low-rank linear model using the right eigenvector of the observed data is given for hyperspectral images. A total variation (TV) based regularization called Low-Rank TV regularization (LRTV) is used for hyperspectral feature extraction. The feature extraction is used for hyperspectral image classification. The classification accuracies obtained are significantly better than the ones obtained using features extracted by Principal Component Analysis (PCA) and Maximum Noise Fraction (MNF).

Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis

The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available.

Wavelet based hyperspectral image restoration using spatial and spectral penalties

In this paper a penalized least squares cost function with a new spatial-spectral penalty is proposed for hyper- spectral image restoration. The new penalty is a combination of a Group LASSO (GLASSO) and First Order Roughness Penalty (FORP) in the wavelet domain. The restoration criterion is solved using the Alternative Direction Method of Multipliers (ADMM). The results are compared with other restoration methods where the proposed method outperforms them for the simulated noisy data set based on Signal to Noise Ratio (SNR) and visually outperforms them on a real degraded data set.