Scene-Level Geographic Image Classification Based on a Covariance Descriptor Using Supervised Collaborative Kernel Coding

Scene-level geographic image classification has been a very challenging problem and has become a research focus in recent years. This paper develops a supervised collaborative kernel coding method based on a covariance descriptor (covd) for scene-level geographic image classification. First, covd is introduced in the feature extraction process and, then, is transformed to a Euclidean feature by a supervised collaborative kernel coding model. Furthermore, we develop an iterative optimization framework to solve this model. Comprehensive evaluations on public high-resolution aerial image dataset and comparisons with state-of-the-art methods show the superiority and effectiveness of our approach

Bilateral Filter Regularized L2 Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

Hyperspectral unmixing (HU) is one of the most active hyperspectral image (HSI) processing research fields, which aims to identify the materials and their corresponding proportions in each HSI pixel. The extensions of the nonnegative matrix factorization (NMF) have been proved effective for HU, which usually uses the sparsity of abundances and the correlation between the pixels to alleviate the non-convex problem. However, the commonly used L 1 / 2 sparse constraint will introduce an additional local minima because of the non-convexity, and the correlation between the pixels is not fully utilized because of the separation of the spatial and structural information. To overcome these limitations, a novel bilateral filter regularized L 2 sparse NMF is proposed for HU. Firstly, the L 2 -norm is utilized in order to improve the sparsity of the abundance matrix. Secondly, a bilateral filter regularizer is adopted so as to explore both the spatial information and the manifold structure of the abundance maps. In addition, NeNMF is used to solve the object function in order to improve the convergence rate. The results of the simulated and real data experiments have demonstrated the advantage of the proposed method

Distributed Attitude Consensus for Multiple Rigid Spacecraft under Jointly Connected Switching Topologies

We study the distributed leader-following attitude consensus problem for multiple rigid spacecraft with a single leader under jointly connected switching topologies. Two cases are considered, where the first case is with a static leader and the second case is with a dynamic leader. By constructing an auxiliary vector and a distributed observer for each follower spacecraft, the controllers are designed to drive all the attitudes of the follower spacecraft to the leader’s, respectively, for both of the two cases, though there are some time intervals in which the communication topology is not connected. The whole system is proved to be stable by using common Lyapunov function method. Finally, the theoretical result is illustrated by numerical simulations

Protective Effects of Chicken Egg Yolk Immunoglobulins (IgYs) against Vibrio vulnificus Infections

Vibrio (V.) vulnificus infection is a rare disease whose death rates exceed 50% despite aggressive antibiotic treatment and surgical debridement. The aim of this study was to assess the ability of specific anti-V. vulnificus immunoglobulins Y (IgYs) for preventing and treating V. vulnificus infections. IgYs were produced by immunizing egg laying hens with inactivated whole cell bacteria. Peritoneal cytokines, blood’s bacterial load, and survival curves were obtained from both prophylactic and therapeutic mouse models. The results showed that the specific IgYs (i) inhibited the growth of V. vulnificus in vitro, (ii) dramatically reduced the inflammatory response and blood’s bacterial load, and (iii) improved the survival rate of V. vulnificus-infected mice. These results prove that anti-V. vulnificus IgYs can be markedly effective means for the prophylaxis and the therapy of V. vulnificus infections