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Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed. The process was formulated as a graph-regularized row-sparse constrained optimization problem, which select a few representative bands to code the all bands based on the learned pairwise band agreement. In RBS-LPA, a spatial-spectral informative graph, constructed by spatial-spectral neighbor relationship, is incorporated to encode both the spatial and spectral geometrical structure. By combining the learning procedure with graph regularizer jointly, the graph regularizer can adaptively change with the sparse representative bands, which ensures that the selected bands can well preserve the local manifold structure. Experimental results on three real urban hyperspectral data demonstrate the efficiency of the proposed RBS-LPA and achieve convincing performance.

With the development of artificial intelligence, multiagent algorithms have been applied to many real-time strategy games. Making plans on the human being is gradually passing away, especially in combat scenarios. Cognitive electronic warfare (CEW) is a complex and challenging work due to the sensitivity of the data sources. There are few studies on CEW. In the past, wargame simulations depended on differential equations and war theory, which resulted in high time and human resource costs. In the future, as other artificial intelligence theories are developed, artificial intelligence will play a more critical role in wargames. The capabilities of multiagent modeling to describe complex systems and predict actions in dynamic environments are superior to those of traditional methods. In this paper, we use a 3D wargame engine from China Aerospace System Simulation Technology Co., Ltd. (Beijing) named All Domain Simulation (ACS), which supports land, sea, and air combat scenarios, to simulate combat. In the simulations, there are several unmanned air vehicles (UAVs) as attackers and several radar stations as defenders, and both have the ability to detect the others. In the game, several UAVs need to learn to detect targets and track targets separately, and we train the UAV's behavior by well-designed reward shaping and multiagent reinforcement learning (MARL) with LSTM. We improved the RDPG algorithm and merged the MADDPG and RDPG algorithms. From the experimental results, we can see that the effectiveness and accuracy of the algorithm have been greatly improved.

At present, low emissivity coating has been widely used in various fields, but damage will greatly reduce the efficiency of low emissivity coating, so the damage detection of low emissivity coating becomes an important work. Based on convolution neural network, a model for automatic identification of coating damage with low emissivity is proposed. Firstly, the optical image data set of low emissivity coating is constructed and extended by means of data enhancement. After that, VGG-19 and ResNet-50 models are built based on tensorflow, and the cross entropy loss function is used in the models. Then, SGD, momentum, RMSprop and Adam are used to optimize the model. In the process of model optimization, the learning rate is adjusted to get the optimal model. The results show that when the learning rate is $5\times 10^{-5}$ and Adam method is used to optimize the model, the recognition accuracy of VGG-19 model is 90.64%, while that of ResNet-50 model is 94.14%. This paper is of great significance for the study of automatic damage identification of low emissivity coatings.

In this article, a multiple diffuse coding metasurface (MDCM) of independent polarization is designed to control the propagation direction of diffuse reflections under different polarizations and to improve the monostatic and bistatic RCS (radar cross section) reduction effect. First, a method for controlling the distribution range and propagation direction of the diffuse field is studied, and the diffuse field distribution of the random phase metasurface is optimized by a genetic algorithm to improve the uniformity of the diffuse scattering distribution. Then, the random phase distribution is superimposed on the periodic gradient phase distributions of the linear and hedge types in the orthogonal direction so that the main propagation direction of the diffuse metasurface deviates from the specular reflection region under different polarizations, showing single and two diffuse beams. Finally, the anisotropic unit cell with a rectangle inside and an improved Jerusalem cross on the outside is employed as the basic coding element of the MDCM due to its independent polarization phase response. The numerical and experimental results show that the MDCM features multiple diffuse scattering, independent polarization and angle insensitivity and can efficiently improve the monostatic and bistatic RCS reduction effect simultaneously. Because the scattered energies are redirected away from the specular reflection direction, the specular scattering reduction effect is better than the isotropic diffuse metasurface. The proposed method increases the difficulty of detection by single or netted radar and has the potential for the applications of stealth techniques.