Polarimetric Synthetic Aperture Radar Image Classification by a Hybrid Method

Different methods proposed so far for accurate classification of land cover types in polarimetric synthetic aperture radar (SAR) image are data specific and no general method is available. A novel hybrid framework for this classification was developed in this work. A set of effective features derived from the coherence matrix of polarimetric SAR data was proposed. Constituents of the feature set are wavelet, texture, and nonlinear features. The proposed feature set has a strong discrimination power. A neural network was used as the classification engine in a unique way. By exploiting the speed of the conjugate gradient method and the convergence rate of the Levenberg-Marquardt method (near the optimal point), an overall speed up of the classification procedure was achieved. Principal component analysis (PCA) was used to shrink the dimension of the feature vector without sacrificing much of the classification accuracy. The proposed approach is compared with the maximum likelihood estimator (MLE) based on the complex Wishart distribution and the results show the superiority of the proposed method, with the average classification accuracy by the proposed method (95.4%) higher than that of the MLE (93.77%). Use of PCA to reduce the dimensionality of the feature vector helps reduce the memory requirements and computational cost, thereby enhancing the speed of the process.     

Fast Fractal Image Encoding Using an Improved Search Scheme

As fractal image encoding algorithms can yield high-resolution reconstructed images at very high compression ratio, and therefore, have a great potential for improving the efficiency of image storage and image transmission. However, the baseline fractal encoding algorithm requires a great deal of time to complete the best matching search between the range and domain blocks, which greatly limits practical applications of the algorithm. In order to solve this problem, a necessary condition of the best matching search based on an image feature is proposed in this paper. The proposed method can reduce the search space significantly and excludes the most inappropriate domain blocks for each range block before carrying out the best matching search. Experimental results show that the proposed algorithm can produce good quality reconstructed images and requires much less time than the baseline encoding algorithm. Specifically, the new algorithm can speed up encoding by about 85 times with a loss of just 3 dB in the peak signal to noise ratio (PSNR), and yields compression ratios close to 34.     

单层隔振平台抗冲击性能试验设计与分析

以结构简化后的模块化平台作为研究对象,根据水下爆炸设备抗冲击试验设计标准,设计试验工况使其达到GJB150.18—1986规定的最严酷考核工况,对此单层隔振平台进行抗冲击试验并与设计谱输入下的仿真结果进行对比．结果表明：试验设计合理,最严酷工况下谱位移达到设计要求,试验后模块化平台模型外观完好,未出现塑性变形,模块化平台冲击响应计算结果与试验结果较为符合,模块化平台的加速度峰值与谱加速度相较于浮动冲击平台内底减小幅度在95%以上,表明单层隔振平台具有良好抗冲击性能．     

基于动力学模型分块逼近的水下机械臂RBF滑模控制算法研究

本文利用切片理论与Morison方程计算了双关节水下机械臂在水中运动时受到的附加质量力、水阻力和水体流动对机械臂产生的冲击力,在传统机械臂动力学模型的基础上建立了完整的双关节水下机械臂动力学模型.基于推导的水下机械臂动力学模型提出了一种RBF滑模控制策略,采用多个RBF神经网络对水下机械臂动力学模型中的不确定项进行分块...     

水下滑翔机低速螺旋桨的推进效率

为了研究混合驱动型水下滑翔机低速螺旋桨的几何设计参数,了解其与推进效率的关系,通过螺旋桨曲线算法计算桨叶特征点,并建立三维模型,基于升力线理论研究螺旋桨直径、转速、叶宽分布、厚度分布等几何参数对螺旋桨水动力性能的影响,运用流体力学仿真分析软件验证螺旋桨的水动力性能。对比分析结果可知:直径及转速对螺旋桨推进效率影响较大,叶宽及厚度分布对其影响较小;根据流体力学分析可知桨毂对螺旋桨推进效率有一定的影响,同时可得低速情况下推进效率较高的螺旋桨叶片设计参数。结果表明,低载荷螺旋桨在低转速、大直径、小叶宽分布、小厚度分布下的推进效率较高,可达0.73。     

回转状态下导管螺旋桨水动力特性的数值模拟

采用滑移网格技术和计算流体力学方法,在导管螺旋桨所在的流域内求解其N-S方程,以此模拟在作回转运动的水下机器人流场作用下导管螺旋桨的推力特性,观察螺旋桨在这一环境下的水动力现象.计算结果表明:当来流从尾部流向机器人时,对于尾导管螺旋桨,有机器人影响时其推力系数比无机器人时要小,对处于机器人舷侧的螺旋桨,有机器人时其推力系数比无机器人时要大;当来流从首部流向机器人时其情形刚好相反;而当机器人轴线与流向大致垂直时,有无机器人对螺旋桨推力系数的影响不大.     

一种新的图象分割方法

大多数图象处理系统都需将目标从背景中分离出来。本文介绍一种新的自适应门限图象分割方法。门限曲面通过梯度值较大的象素点即可能的边缘点灰度值内插得到。     

从“南海I号”到“南澳I号”

在历经近两年半的水下考古抢救发掘的前期准备之后,沉没于广东汕头南澳县海域的明代沉船“南澳I号”水下考古抢救发掘已于2009年9月26日正式启动。它将会给人们带来怎样的惊喜？与此前赚足媒体与众人眼球的“南海I号”相比,二者又有怎样的不同？     

浅谈桥梁海域钻孔桩施工技术措施

钻孔桩是指在具有泥浆护壁的条件下,使用机械钻进完成桩孔,再利用导管法灌注水下混凝土的一种常见施工方法.这种方法施工难度不大,操作也较易掌握,在桥梁海域施工中应用十分广泛,本文对在桥梁海域施工中钻孔桩技术措施进行了研究.     

水下砼灌注的施工工艺与施工质量控制

文章介绍了水下灌注砼的控制指标,并探讨了灌注的施工工艺要点,然后针对水下灌注砼常见的质量问题提出来控制措施。