In this paper a new hyperspectral image based on wavelets and sparse regularization is proposed. This new method is called Wavelet Based Sparse Restoration (WBSR). The hyperspectral signal is restored by utilizing penalized least squares and the `1 penalty. Iterative Soft Thresholding (IST) algorithm is used to solve the convex optimization problem. It is shown that not only WBSR improves the denioising results both visually and based on Signal to Noise Ratio (SNR) but also increases the classification accuracies.
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.
Classification of high resolution remote sensing data from urban areas is investigated. The main challenge with the classification of high resolution remote sensing image data is that spatial information is extremely important in the classification. Therefore, classification methods for such data need to take that into account. Here, a method based on mathematical morphology is used in order to preprocess the image data. The approach is based on building a morphological profile by a composition of geodesic opening and closing operations of different sizes. In the paper, the classification of is performed on two data sets from urban areas; one panchromatic and one hyperspectral. These data sets have different characteristcs and need different treatments by the morphological
approach. The approach can be directly applied on the panchromatic data. However, some feature extraction needs to be done on the hyperspectral data before the approach is applied. Both principal and independent components are considered here for that purpose. A neural network approach is used for the classification of the morphological profiles and its performance in terms of accuracies is compared to the classification of a fuzzy possibilistic approach in the case of the pancrhomatic data and the conventional maximum likelhood method
based on the Gaussian assumption in the case of the case of hyperspectral. Different types of feature extraction methods are considered in the classification process.
Wavelets have become a popular tool in many research areas because of the combination of a nice theoretical foundation and promising applications. The theoretical foundation reveals new insights and has thrown a new light on several application areas. One of the applications is speckle reduction and enhancement of synthetic aperture radar (SAR) images. The use of wavelet thresholding as noise reduction method is based on the following properties: Wavelet transformation creates a sparse signal (because of the decorrelation property of the transform); noise is spread out equally over all wavelet transform coefficients; noise level is not too high and thus signal wavelet transformation coefficients can be recognized. In this paper, we will review the use of wavelet,translation invariant wavelet, almost translation invariant wavelet (complex wavelet), and multi-wavelet transformations in speckle reduction of SAR images. Several nonlinear thresholding functions, i.e., hard, soft, adaptive sigmoid, and a function based on generalized cross validation are investigated and compared in experiments.
Speckle reduction and enhancement of synthetic aperture radar (SAR) images with multiple wavelets (multiwavelets) are proposed and investigated. Multiwavelet transformations are 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. A non-linear speckle reduction method based on adaptive sigmoid thresholding of the multiwavelet coefficients for logarithmically transformed SAR image data is investigated. The proposed methods show great promise for speckle removal and hence provide good detection performance for SAR based recognition.
Optimized combination, regularization, and pruning is proposed for the Parallel Consensual Neural Networks (PC-NNs) which is a neural network architecture based on the consensus of a collection of stage neural networks trained on the same input data with different representations. Here, a regularization scheme is presented for the PCNN and in training a regularized cost function is minimized. The use of this regularization scheme in conjunction with Optimal Brain Damage pruning is suggested both to optimize the architecture of the individual stage networks and to avoid overfitting. Experiments are conducted on a multisource remote sensing and geographic data set consisting of six data source. The results obtained by the proposed version of PCNN are compared to other classification approaches such as the original PCNN, single stage neural networks and statistical classifiers. In comparison to the originally proposed PCNNs, the use of pruning and regularization not only produces simpler PCNNs but also gives higher classification accuracies. In particular, using the proposed approach, a neural network based non-linear combination scheme, for the individual stages in the PCNN, produces excellent overall classification accuracies for both training and test data.
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