Sketch-based subspace clustering of hyperspectral images

Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SSC-based method, which can effectively process large HSIs while also achieving improved clustering accuracy compared to the current SSC methods. We build our approach based on an emerging concept of sketched subspace clustering, which was to our knowledge not explored at all in hyperspectral imaging yet. Moreover, there are only scarce results on any large-scale SSC approaches for HSI. We show that a direct application of sketched SSC does not provide a satisfactory performance on HSIs but it does provide an excellent basis for an effective and elegant method that we build by extending this approach with a spatial prior and deriving the corresponding solver. In particular, a random matrix constructed by the Johnson-Lindenstrauss transform is first used to sketch the self-representation dictionary as a compact dictionary, which significantly reduces the number of sparse coefficients to be solved, thereby reducing the overall complexity. In order to alleviate the effect of noise and within-class spectral variations of HSIs, we employ a total variation constraint on the coefficient matrix, which accounts for the spatial dependencies among the neighbouring pixels. We derive an efficient solver for the resulting optimization problem, and we theoretically prove its convergence property under mild conditions. The experimental results on real HSIs show a notable improvement in comparison with the traditional SSC-based methods and the state-of-the-art methods for clustering of large-scale images

A robust sparse representation model for hyperspectral image classification

Sparse representation has been extensively investigated for hyperspectral image (HSI) classification and led to substantial improvements in the performance over the traditional methods, such as support vector machine (SVM). However, the existing sparsity-based classification methods typically assume Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust classification model that admits realistic mixed noise, which includes Gaussian noise and sparse noise. We combine a model for mixed noise with a prior on the representation coefficients of input data within a unified framework, which produces three kinds of robust classification methods based on sparse representation classification (SRC), joint SRC and joint SRC on a super-pixels level. Experimental results on simulated and real data demonstrate the effectiveness of the proposed method and clear benefits from the introduced mixed-noise model

Landmark-based large-scale sparse subspace clustering method for hyperspectral images

Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in the clustering of hyperspectral images (HSIs). However, the high computational complexity and sensitivity to noise limit its clustering performance. In this paper, we propose a scalable SSC method for the large-scale HSIs, which significantly accelerates the clustering speed of SSC without sacrificing clustering accuracy. A small landmark dictionary is first generated by applying k-means to the original data, which results in the significant reduction of the number of optimization variables in terms of sparse matrix. In addition, we incorporate spatial regularization based on total variation (TV) and improve this way strongly robustness to noise. A landmark-based spectral clustering method is applied to the obtained sparse matrix, which further improves the clustering speed. Experimental results on two real HSIs demonstrate the effectiveness of the proposed method and the superior performance compared to both traditional SSC-based methods and the related large-scale clustering methods

Sketched sparse subspace clustering for large-scale hyperspectral images

Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in clustering of hyperspectral images. However, the computational complexity of SSC-based methods is prohibitive for large-scale problems. We propose a large-scale SSC-based method, which processes efficiently large-scale HSIs without sacrificing the clustering accuracy. The proposed approach incorporates sketching of the self-representation dictionary reducing thereby largely the number of optimization variables. In addition, we employ a total variation (TV) regularization of the sparse matrix, resulting in a robust sparse representation. We derive a solver based on the alternating direction method of multipliers (ADMM) for the resulting optimization problem. Experimental results on real data show improvements over the traditional SSC-based methods in terms of accuracy and running time

Mesoscopic Interactions and Species Coexistence in Evolutionary Game Dynamics of Cyclic Competitions

Date of Acceptance: 27/11/2014Peer reviewedPublisher PD

In Situ Monitoring Of The Hydration Of Calcium Silicate Minerals In Cement With A Remote Fiber-optic Raman Probe

This study utilized a novel in situ fiber-optic Raman probe to continuously monitor the hydration progress of tricalcium silicate (C3S) and dicalcium silicate (C2S) without the need for sampling, from early hydration stage to later stages, and from fresh to hardened states of paste samples. By virtue of the remarkable ability of this technique in characterizing either dry or wet and crystalline or amorphous samples, the hydration processes of C3S and C2S pastes with different water-to-solid (w/s) ratios could be monitored from the start of the hydration reaction. The main hydration products, calcium silicate hydrate (C–S–H) and portlandite/calcium hydroxide (CH), have been successfully identified and continuously monitored for variations in their respective amounts in situ. The effect of w/s ratio on the hydration processes of C3S and C2S pastes was also considered. Meanwhile, the x-ray diffraction (XRD) and thermogravimetric analysis (TGA) results showed a great correlation with the in situ Raman test results about hydration products, which demonstrated the reliability of this technology. Moreover, the signal-to-noise ratio (SNR) of this Raman probe is significantly superior to existing technologies for in situ fiber-optic Raman spectroscopy. This remote fiber-optic Raman probe enables the use of Raman spectroscopy in future construction projects for on-site monitoring and evaluation of health conditions and performance of concrete structures

Assembly of normal actomyosin rings in the absence of Mid1p and cortical nodes in fission yeast

Cytokinesis in many eukaryotes depends on the function of an actomyosin contractile ring. The mechanisms regulating assembly and positioning of this ring are not fully understood. The fission yeast Schizosaccharomyces pombe divides using an actomyosin ring and is an attractive organism for the study of cytokinesis. Recent studies in S. pombe (Wu, J.Q., V. Sirotkin, D.R. Kovar, M. Lord, C.C. Beltzner, J.R. Kuhn, and T.D. Pollard. 2006. J. Cell Biol. 174:391–402; Vavylonis, D., J.Q. Wu, S. Hao, B. O'Shaughnessy, and T.D. Pollard. 2008. Science. 319:97–100) have suggested that the assembly of the actomyosin ring is initiated from a series of cortical nodes containing several components of this ring. These studies have proposed that actomyosin interactions bring together the cortical nodes to form a compacted ring structure. In this study, we test this model in cells that are unable to assemble cortical nodes. Although the cortical nodes play a role in the timing of ring assembly, we find that they are dispensable for the assembly of orthogonal actomyosin rings. Thus, a mechanism that is independent of cortical nodes is sufficient for the assembly of normal actomyosin rings

Conjugate Calculation of Gas Turbine Vanes Cooled with Leading Edge Films

AbstractConjugate calculation methodology is used to simulate the C3X gas turbine vanes cooled with leading edge films of “shower-head” type. By comparing calculated results of different turbulence models with the measured data, it is clear that calculation with the transition model can better simulate the flow and heat transfer in the boundary layers with leading edge film cooling. In the laminar boundary layers, on the upstream suction side, the film cooling flow presents 3D turbulent characteristics before transition, which quickly disappear on the downstream suction side owing to its intensified mixing with hot gas boundary layer after transition. On the pressure side, the film cooling flow retains the 3D turbulent characteristics all the time because the local boundary layers' consistent laminar flow retains a smooth mixing of the cooling flow and the hot gas. The temperature gradients formed between the cooled metallic vane and the hot gas can improve the stability of the boundary layer flow because the gradients possess a self stable convective structure