scholarly journals Anomaly Detection for Hyperspectral Images Based on Anisotropic Spatial-Spectral Total Variation and Sparse Constraint

2018 ◽  
Vol 173 ◽  
pp. 03084
Author(s):  
Ju Huihui ◽  
Liu Zhigang ◽  
Jiangjun Jiang ◽  
Yang Wang
Keyword(s):  
Anomaly Detection ◽  
Total Variation ◽  
Spatial Dimension ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Total Variation Regularization ◽  
Spectral Variation ◽  
Sparse Constraint ◽  
Split Bregman ◽  
Cubic Form

A novel anomaly detection method for hyperspectral images (HSIs) is proposed based on anisotropic spatial-spectral total variation and sparse constraint. HSIs are assumed to be not only smooth in spectral dimension but also piecewise smooth in spatial dimension. The proposed method adopts the anisotropic spatial-spectral total variation model which combines 2D spatial total variation and 1D spectral variation to explore the spatial-spectral smooth property of HSIs. Meanwhile, the sparse property of anomalies is exploited for its low probability in the image. To utilize both the spatial and spectral information of HSIs, we preserve the original cubic form of HSIs and divide the HSIs into three 3D arrays, each representing the background, the anomaly, and the noise respectively. By using anisotropic spatial-spectral total variation regularization on the background component and sparse constraint on the anomaly component, this anomaly detection problem has therefore been formulated as a constraint optimization problem whose solution has been derived by alternately using Split Bregman Method and Go Decomposition (GoDec) Method. Experimental results on hyperspectral datasets illustrate that our proposed method has a better detection performance than state-of-the-art hyperspectral anomaly detection methods.

scholarly journals A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3627 ◽  
Author(s):  
Yi Zhang ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yan Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Sparse Representation ◽  
Parallel Algorithm ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Detection Methods ◽  
Matrix Computations ◽  
Hyperspectral Image Processing ◽  
Mapreduce Model

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

Feature Extraction Techniques for Airborne Hyperspectral Images – Implication for Mineral Exploration

2021 ◽  
Author(s):  
Rupsa Chakraborty ◽  
Gabor Kereszturi ◽  
Reddy Pullanagari ◽  
Patricia Durance ◽  
Salman Ashraf ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Total Variation ◽  
Spatial Resolution ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Total Variation Regularization ◽  
Elemental Distribution ◽  
Ground Truth Data ◽  
Resolution Data ◽  
Mineral Prospecting

<p>Geochemical mineral prospecting approaches are mostly point-based surveys which then rely on statistical spatial extrapolation methods to cover larger areas of interest. This leads to a trade-off between increasing sampling density and associated attributes (e.g., elemental distribution). Airborne hyperspectral data is typically high-resolution data, whilst being spatially continuous, and spectrally contiguous, providing a versatile baseline to complement ground-based prospecting approaches and monitoring. In this study, we benchmark various shallow and deep feature extraction algorithms, on airborne hyperspectral data at three different spatial resolutions, 0.8 m, 2 m and 3 m. Spatial resolution is a key factor to detailed scale-dependent mineral prospecting and geological mapping. Airborne hyperspectral data has potential to advance our understanding for delineating new mineral deposits. This approach can be further extended to large areas using forthcoming spaceborne hyperspectral platforms, where procuring finer spatial resolution data is highly challenging. The study area is located along the Rise and Shine Shear Zone (RSSZ) within the Otago schist, in the South Island (New Zealand). The RSSZ contains gold and associated hydrothermal sulphides and carbonate minerals that are disseminated through sheared upper green schist facies rocks on the 10-metre scale, as well as localized (metre-scale) quartz-rich zones. Soil and rock samples from 63 locations were collected, scattered around known mineralised and unmineralized zones, providing ground truth data for benchmarking. The separability between the mineralized and the non-mineralised samples through laboratory based spectral datasets was analysed by applying Partial least squares discriminant analysis (PLS-DA) on the XRF spectra and laboratory based hyperspectral data separately. The preliminary results indicate that even in partially vegetated zones mineralised regions can be mapped out relatively accurately from airborne hyperspectral images using orthogonal total variation component analysis (OTVCA). This focuses on feature extraction by optimising a cost function that best fits the hyperspectral data in a lower dimensional feature space while monitoring the spatial smoothness of the features by applying total variation regularization.</p>

scholarly journals Performance of relaxed iterative methods for image deblurring problems

2019 ◽  
Vol 13 ◽  
pp. 174830261986173 ◽  
Author(s):  
Jae H Yun
Keyword(s):  
Fixed Point ◽  
Iterative Methods ◽  
Total Variation ◽  
Numerical Experiments ◽  
Krylov Subspace ◽  
Image Deblurring ◽  
Total Variation Regularization ◽  
L1 Norm ◽  
Split Bregman ◽  
Relaxation Parameters

In this paper, we consider performance of relaxation iterative methods for four types of image deblurring problems with different regularization terms. We first study how to apply relaxation iterative methods efficiently to the Tikhonov regularization problems, and then we propose how to find good preconditioners and near optimal relaxation parameters which are essential factors for fast convergence rate and computational efficiency of relaxation iterative methods. We next study efficient applications of relaxation iterative methods to Split Bregman method and the fixed point method for solving the L1-norm or total variation regularization problems. Lastly, we provide numerical experiments for four types of image deblurring problems to evaluate the efficiency of relaxation iterative methods by comparing their performances with those of Krylov subspace iterative methods. Numerical experiments show that the proposed techniques for finding preconditioners and near optimal relaxation parameters of relaxation iterative methods work well for image deblurring problems. For the L1-norm and total variation regularization problems, Split Bregman and fixed point methods using relaxation iterative methods perform quite well in terms of both peak signal to noise ratio values and execution time as compared with those using Krylov subspace methods.

scholarly journals An Adaptive Image Denoising Model Based on Tikhonov and TV Regularizations

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Kui Liu ◽  
Jieqing Tan ◽  
Benyue Su
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Tikhonov Regularization ◽  
Experimental Results ◽  
Total Variation Regularization ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Gradient Information ◽  
Weighted Combination ◽  
Staircase Artifacts

To avoid the staircase artifacts, an adaptive image denoising model is proposed by the weighted combination of Tikhonov regularization and total variation regularization. In our model, Tikhonov regularization and total variation regularization can be adaptively selected based on the gradient information of the image. When the pixels belong to the smooth regions, Tikhonov regularization is adopted, which can eliminate the staircase artifacts. When the pixels locate at the edges, total variation regularization is selected, which can preserve the edges. We employ the split Bregman method to solve our model. Experimental results demonstrate that our model can obtain better performance than those of other models.

Data-driven multichannel seismic impedance inversion with anisotropic total variation regularization

2018 ◽  
Vol 26 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Dehua Wang ◽  
Jinghuai Gao ◽  
Hongan Zhou
Keyword(s):  
Total Variation ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Data Driven ◽  
Total Variation Regularization ◽  
Split Bregman ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Impedance Inversion ◽  
Anisotropic Total Variation

AbstractAcoustic impedance (AI) inversion is a desirable tool to extract rock-physical properties from recorded seismic data. It plays an important role in seismic interpretation and reservoir characterization. When one of recursive inversion schemes is employed to obtain the AI, the spatial coherency of the estimated reflectivity section may be damaged through the trace-by-trace processing. Meanwhile, the results are sensitive to noise in the data or inaccuracies in the generated reflectivity function. To overcome the above disadvantages, in this paper, we propose a data-driven inversion scheme to directly invert the AI from seismic reflection data. We first explain in principle that the anisotropic total variation (ATV) regularization is more suitable for inverting the impedance with sharp interfaces than the total variation (TV) regularization, and then establish the nonlinear objective function of the AI model by using anisotropic total variation (ATV) regularization. Next, we solve the nonlinear impedance inversion problem via the alternating split Bregman iterative algorithm. Finally, we illustrate the performance of the proposed method and its robustness to noise with synthetic and real seismic data examples by comparing with the conventional methods.

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