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In hyperspectral image processing technologies, anomaly detection is a valuable and practical way of searching small unknown targets based on spectral characteristics. For the lack of prior knowledge of targets, background modeling on hyperspectral images is the key process that affects the outcome of anomaly detection operator. In this paper, a novel method of anomaly detection based on quadratic...
Recovering hyperspectral image (HSI) from mixed noise degradation is a challenging and promising theme in remote sensing, particularly when stripes and deadlines exist in several contiguous bands. This paper proposes a HSI’s restoration method making use of adaptive morphological filtering (AMF) and fusing structure information of an auxiliary color image. An adaptive structuring element (ASE) indicating...
Underdetermined blind source separation (UBSS) deals with the problem of estimating n source signals from m measurements (n > m), with an unknown mixing process. Most researches pay attention to the sparsity of speech to recover source signals, such as the DUET (degenerate unmixing estimation technique) algorithm, which can separate any number of sources using only two mixtures with the help of...
We consider the dictionary learning problem in sparse representations based on an analysis model with noisy observations. A typical limitation associated with several existing analysis dictionary learning (ADL) algorithms, such as Analysis K-SVD, is their slow convergence due to the procedure used to pre-estimate the source signal from the noisy measurements when updating the dictionary atoms in each...
Aiming at the problem of load balancing and lifetime prolonging for wireless sensor networks (WSNs), and considering complex uncertainties existed in WSNs, this paper proposes a clustering routing protocol CRT2FLACO for WSN based on type-2 fuzzy logic and ant colony optimization (ACO). Specifically, in the cluster set-up phase, a type-2 Mamdnai fuzzy logic system (T2MFLS) is built to handle rule uncertainty...
Analysis dictionary learning (ADL) aims to adapt dictionaries from training data based on an analysis sparse representation model. In a recent work, we have shown that, to obtain the analysis dictionary, one could optimise an objective function defined directly on the noisy signal, instead of on the estimated version of the clean signal as adopted in analysis K-SVD. Following this strategy, a new...
In this paper, the Potential Function Agglomeration Clustering (PFAC) algorithm has been proposed for estimating the mixing matrix in underdetermined Sparse Component Analysis (SCA), wherein the number of mixtures is less than the number of the sources. In contrast to many existing SCA methods, the PFAC algorithm can accurate estimate the number of sources and the mixing matrix. The algorithm also...
In this paper, a robust K-plane clustering algorithm has been proposed for blind separation of underdetermined mixtures of sparse sources. In the presence of noise, based on the insufficient sparsity assumption of the source signals, the K-dimensional concentration hyperplanes have been found by using the algorithm, and then using them to estimate the mixing matrix. Simulation results show that the...
In this paper, we will present an effective improved parallel hybrid asynchronous preconditioned GMRES method implemented on a nation wide grid environment. The classic restarted GMRES method is used widely to solve the large sparse linear systems. In order to accelerate the convergence, we use Arnoldi method to compute Ritz elements in parallel to optimize the computation of a polynomial. These two...
The method GMRES is used widely to solve the large sparse linear systems. In this paper, we will present an effective parallel hybrid asynchronous method, which combines the typical parallel method GMRES with the Least Square method that needs some eigenvalues obtained from a parallel Arnoldi process. And we will apply it on a Grid Computing platform Grid5000. Grid computing in general is a special...
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