针对如何充分利用空间特征来达到较高的高光谱图像分类精度的问题,提出了一种基于三维离散小波变换(3D-DWT)与随机补丁网络(RPNet)结合的高光谱图像的地物属性分类算法。在分类过程中,综合3D-DWT提取的特征和RPNet深度学习框架提取的特...针对如何充分利用空间特征来达到较高的高光谱图像分类精度的问题,提出了一种基于三维离散小波变换(3D-DWT)与随机补丁网络(RPNet)结合的高光谱图像的地物属性分类算法。在分类过程中,综合3D-DWT提取的特征和RPNet深度学习框架提取的特征,利用支持向量机(SVM)对特征向量进行分类。所提出的方法在Indian Pines和University of Pavia两个数据集上进行测试,结果表明该方法比现有方法有显著的分类性能的提高。展开更多
Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly ...Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly analyzes and obtains the average delay for all peers in the whole transmitting process due to the limitation of network throughput, and then proposes a mathematical model for the propagation of passive worms over the unstructured P2P networks. The model mainly takes the effect of the network throughput into account, and applies a new healthy files dissemination-based defense strategy according to the file popularity which follows the Zipf distribution. The simulation results show that the propagation of passive worms is mainly governed by the number of hops, initially infected files and uninfected files. The larger the number of hops, the more rapidly the passive worms propagate. If the number of the initially infected files is increased by the attackers, the propagation speed of passive worms increases obviously. A larger size of the uninfected file results in a better attack performance. However, the number of files generated by passive worms is not an important factor governing the propagation of passive worms. The effectiveness of healthy files dissemination strategy is verified. This model can provide a guideline in the control of unstructured P2P networks as well as passive worm defense.展开更多
文摘针对如何充分利用空间特征来达到较高的高光谱图像分类精度的问题,提出了一种基于三维离散小波变换(3D-DWT)与随机补丁网络(RPNet)结合的高光谱图像的地物属性分类算法。在分类过程中,综合3D-DWT提取的特征和RPNet深度学习框架提取的特征,利用支持向量机(SVM)对特征向量进行分类。所提出的方法在Indian Pines和University of Pavia两个数据集上进行测试,结果表明该方法比现有方法有显著的分类性能的提高。
基金National Natural Science Foundation of China (No.60633020 and No. 90204012)Natural Science Foundation of Hebei Province (No. F2006000177)
文摘Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly analyzes and obtains the average delay for all peers in the whole transmitting process due to the limitation of network throughput, and then proposes a mathematical model for the propagation of passive worms over the unstructured P2P networks. The model mainly takes the effect of the network throughput into account, and applies a new healthy files dissemination-based defense strategy according to the file popularity which follows the Zipf distribution. The simulation results show that the propagation of passive worms is mainly governed by the number of hops, initially infected files and uninfected files. The larger the number of hops, the more rapidly the passive worms propagate. If the number of the initially infected files is increased by the attackers, the propagation speed of passive worms increases obviously. A larger size of the uninfected file results in a better attack performance. However, the number of files generated by passive worms is not an important factor governing the propagation of passive worms. The effectiveness of healthy files dissemination strategy is verified. This model can provide a guideline in the control of unstructured P2P networks as well as passive worm defense.
