UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification

The paper proposes an upgraded landmark-Isometric mapping (UL-Isomap) method to solve the two problems of landmark selection and computational complexity in dimensionality reduction using landmark Isometric mapping (LIsomap) for hyperspectral imagery (HSI) classification. First, the vector quantization method is introduced to select proper landmarks for HSI data. The approach considers the variations in local density of pixels in the spectral space. It locates the unique landmarks representing the geometric structures of HSI data. Then, random projections are used to reduce the bands of HSI data. After that, the new method incorporates the Recursive Lanczos Bisection (RLB) algorithm to construct the fast approximate k-nearest neighbor graph. The RLB algorithm accompanied with random projections improves the speed of neighbor searching in UL-Isomap. After constructing the geodesic distance graph between landmarks and all pixels, the method uses a fast randomized low-rank approximate method to speed up the eigenvalue decomposition of the inner-product matrix in multidimensional scaling. Manifold coordinates of landmarks are then computed. Manifold coordinates of non-landmarks are computed through the pseudo inverse transformation of landmark coordinates. Five experiments on two different HSI datasets are run to test the new UL-Isomap method. Experimental results show that UL-Isomap surpasses LIsomap, both in the overall classification accuracy (OCA) and in computational speed, with a speed over 5 times faster. Moreover, the UL-Isomap method, when compared against the Isometric mapping (Isomap) method, obtains only slightly lower OCAs.     

Viterbi译码算法在GMSK系统中的性能分析

通过研究译码软、硬判决实现理论,比较在GMSK系统中两种判决对误码率的差别,在Simulink中设计基于GMSK调制方式、卷积编码、软判决与硬判决译码相对比的通信仿真系统,分析不同信噪比对误码率的影响.仿真结果证明了所建立系统的正确性,说明使用GMSK调制方式,在误码率为10-3 -10-2区间内,使用软判决要比硬判决译码提升信噪比约3 dB.因此,数传电台采用Viterbi译码是保证地震信号传输质量的有效方法.     

基于灰度共生矩阵的DEM地形纹理特征量化研究

 DEM的地形纹理以其表达地形表面的纯粹性与分析数据的可派生性受到越来越多关注。本文选取陕西省10个不同地貌类型区的25m分辨率DEM数据,引入空间灰度共生矩阵(GLCM)对地形表面纹理特征进行定量分析。研究表明,25m分辨率DEM数据的GLCM模型适宜分析间距是大于等于3个栅格大小。各纹理参数中,相关度可用于地形纹理的方向性量化;方差、差的方差、对比度可用于对地形纹理的周期性分析;熵、二阶角矩、逆差矩可用于对地形纹理的复杂性分析。在DEM及其派生数据中,光照模拟数据计算的各纹理参数的平均变异系数最高,表明光照模拟数据最适合于地形纹理特征的量化研究。同时本文提出了一种多参数综合的地形纹理量化方法,通过运用综合周期性和综合复杂性两个指标对不同地形区量化分析,结果表明,这两个指标对不同地形形态响应显著,可用于地形形态分类与识别研究。     

一种基于六角格的越野通行方法

为了解决车辆的越野路径规划问题,分析了影响车辆越野通行的主要影响因子,提出了采用六角格网为基础格网量化地形的数据模型,研究了六角格网的空间数据结构及六角格网属性信息的建模过程。对影响车辆越野通行的影响因素进行了分类分析,重点研究了地形、道路、坡度等对车辆通行等级的影响,并且提出了多因子综合分析方法,建立了多因子综合作用下的越野机动通行性分析模型。     

Recognizing spatial distribution patterns of grassland insects: neural network approaches

The main objective of this study was to fit and recognize spatial distribution patterns of grassland insects using various neural networks, and to analyze the feasibility of neural networks for detecting spatial distribution patterns of grassland insects. BP neural network, Learning vector quantization (LVQ) neural network, linear neural network and Fisher’s linear discriminant analysis were used to fit and recognize spatial distribution patterns at different ecological scales. Various comparisons and analysis were conducted. The results showed that BP, LVQ and linear neural networks were better algorithms for recognizing spatial distribution patterns of grassland insects. BP neural network was the best algorithm to fit spatial distribution patterns. BP network may be used to recognize the spatial details of distribution patterns, and the recognition performance of BP network became better as the increase of the number of hidden layers and neurons. Performance of linear neural network for pattern recognition was similar to linear discrimination method. Linear neural network would yield better performance in finding the general trends of distribution patterns. Recognition performance of LVQ network was just between BP network and linear network. It was found that recognition performance of neural networks depended upon not only the ecological scale but also the criterion for classification. Under the uniform criterion, recognition efficiency of linear methods tended to be weak as ecological scale became to be coarser. A joint use of neural networks was suggested in order to achieve both overall and detailed understanding on spatial distribution patterns.     

一种提高静止图像压缩比的压缩算法

作者在已有的基于DCT变换的双VQ静止图像算法的基础上提出了的改进算法，利用神经网络中的自组织特征映射神经网络进行图象矢量量化的码书设计，通过实验表明本算法在进一步提高压缩比的同时可以得到令人满意的图像效果。     

一种实用的遥感影像二维信息隐藏盲算法

提出了一种实用的二维信息隐藏盲算法,以实现遥感影像中机密信息的隐藏。     

地形变数字观测基础

介绍了将地形变模拟信号变换成数字信号过程中所作的采样、保持、量化与传输等环节，地采样过程的原理及其物理含义作了详细的阐述，并以对固体潮连续信号的采样为例作了说明。     

基于修正嵌入式容积卡尔曼滤波的相位展开算法

针对干涉图展开问题,将Levenberg-Marquardt方法修正后的嵌入式容积卡尔曼滤波器应用于缠绕相位图像的展开中,结合基于修正矩阵束模型的局部相位梯度估计算法以及量化跟踪策略,提出一种基于修正嵌入式容积卡尔曼滤波的相位展开算法。该算法不仅可以精确和快速地展开缠绕像元,还可以在展开缠绕像元的同时抑制相位噪声,降低前置预滤波器的难度与复杂度,甚至可以在处理受噪声污染不严重的干涉图时免去前置预滤波处理步骤。试验结果表明本文算法具有较高的效率和良好的稳键性。     

遥感影像目视解译矢量化分析

遥感技术已成为快速、客观、准确监测土地利用变化状况及其变化规律的重要手段.在土地调查工作中,具有十分重要的研究意义和使用价值,也是十分可行的一种技术手段.文章通过第二次土调查遥感影像解译及矢量化实际应用,阐述了遥感影像在二调中的应用,论证了遥感影像在第二次土地调查应用中的方法,得出了在土地调查工作中遥感影像的应用具有十...