一种多尺度高光谱影像小波分形维特征计算方法

提出了一种基于高光谱曲线小波分形测度的高光谱影像多尺度分形维特征分析方法。对高光谱影像的光谱响应曲线的小波域高频和低频系数统计特性、分形特征进行了分析,提出以小波低频分形维表征原始光谱曲线分形特征,以小波系数高频分形维表征高光谱细节特征方法,设计了基于高光谱曲线小波分形维的多尺度特征计算算法,实验结果表明,小波分形维值可有效表征丰富的光谱特征,可用于高光谱影像特征提取和分类。     

基于分形信号的高光谱影像增强方法

目前常用的高光谱影像增强方法大多继承了多光谱影像的增强处理方法,这类方法没有充分利用光谱信息,而基于混合像元分解的图像增强方法存在端元的选取问题。基于影像的自相似特征,探索运用分形信号进行遥感影像增强的可能性。以3景Hyperion高光谱影像数据为基础,把基于地毯的方法进行修正后用于计算高光谱影像中每一像元的分形信号。结果表明,与原始高光谱影像相比,分形信号影像可以更好地突出地物特征,从而达到影像增强的目的,原始曲线形态特征、初始尺度的选择以及采样点数目对分形信号和分形特征尺度均有影响。     

基于光谱曲线小波系数分维测度的高光谱影像高效分割方法

针对高光谱遥感影像处理效率的问题,提出了一种基于高光谱曲线小波分解低频系数分维特征影像和高频系数分维特征影像相结合的高光谱遥感影像分割方法。对高光谱响应曲线的分形测度进行了分析,提出基于光谱曲线小波分解高频系数的分维算法,得到多尺度高光谱分形特征影像。设计了低频系数分维特征影像和高频系数分维特征影像相结合的高光谱影像分割算法。高光谱曲线小波系数分维特征影像分割实验结果表明:该算法可取得与光谱曲线直接分形测度特征影像分割一致结果,但效率优于直接分维特征影像分割。     

基于光谱角和光谱距离评价指标的遥感影像融合方法比较研究——以QuickBird数据为例

遥感数据融合是一种得到具有较高空间分辨率和光谱分辨率数据的有效方法,而如何保持地物光谱特性是遥感数据融合的关键问题。QuickBird卫星数据是高空间分辨率遥感数据的典型代表,探讨QuickBird数据的融合方法对于促进该数据的广泛应用具有重要意义,同时也能为其他高分辨率数据的处理提供借鉴。以QuickBird高分辨遥感数据为例,比较研究了目前针对高分辨率遥感数据常用的高通滤波、小波变换、Gram\|Schmidt和Pan\|sharpening 4种融合方法,以反映光谱曲线变化程度的光谱角和光谱距离为指标,评价了4种融合方法对多光谱影像地物光谱信息的保持能力。结果表明:小波变换在显著提高空间分辨率的同时最大程度地保持了原始多光谱影像的光谱信息,是4种方法中最适合QuickBird遥感数据的融合方法。     

基于LSMM模型的精确地物覆盖类型的提取方法

本文对线性光谱混合模型(LSMM)进行了研究，提出了利用分解误差和比值系数分别进行约束，从而提取精确的地物覆盖类型的研究方法，并利用模拟数据和实际数据进行了验证。实验结果表明，相对于传统的需要对地物进行逻辑上或光谱组成上归并的研究方法，采用本方法提取更为细致的地物覆盖类型，是可能的。     

基于小波变换的气温趋势和分形特征分析

将小波变换、多重分形和R/S分析法相结合,选择db4小波函数进行小波变换,利用分解与重构算法,从分形维数、非周期循环长度、奇异性指数、多重分形谱等方面探讨合肥市近54年来气温系统的动力学分形特性和演化规律,分析合肥市气温趋势的持续性、长期记忆性、统计自相似性和多重分形性。分析结果表明,合肥市气温变化过程是一个层次分明的过程,具有多标度结构且1月、8月、11月的分形维数较高,气温不规则程度高,复杂性高。     

一种基于小波变换的分形零树混合编码方法

为了降低分形编码的复杂度和提高编码效率 ,提出了一种基于小波变换的分形零树混合图象编码方法 .该方法首先利用小波变换将图象分解为不同方向不同分辨率的子图象 ,然后分别在不同分辨率层将这些子图象以类似于零树的结构 ,构成一棵棵的小波子树 (wavelet subtree) ,最后在编码时 ,对每一棵小波子树进行具体的分析 ,或采用分形编码 (fractal coding) ,或采用零树编码 (zerotree coding) .与传统的基于小波变换的分形编码相比 ,此算法在充分利用子带图象间的相似性和块内存在的自相似性的同时 ,也充分利用了小波变换后子图象块内 ,特别是高分辨层的子图象块内所存在的大量局域性的冗余性 .实验结果表明 ,此方法在较大的压缩范围内 ,都能够获得较好的压缩效果 .     

基于局部连续性与全局相似性的光谱保持型亚像元映射

遥感图像的像元级分类精度受混合像元的影响. 亚像元映射以像元分解获得的丰度值为基础,在地物分布规律的约束下,细化估计各类地物的亚像元级分布模式. 本文同时考虑了地物分布的空间与光谱信息,提出了一种基于局部连续性与全局相似性的光谱保持型亚像元映射算法. 针对地物的空间分布特性,提出了利用类内离散度对局部连续性进行建模,并通过相似分布像元表示误差引入全局相似性约束项. 针对地物的光谱特性,采用最小化光谱误差约束了亚像元映射过程中的光谱无失真性. 模拟数据与真实数据上的实验结果表明,本文算法比其他同类算法具有更高的估计精度,且更适合于实际应用.     

高光谱遥感数据光谱特征提取算法与分类研究*

针对高光谱数据的特点,探讨了高光谱数据特征提取的若干算法,重点研究了导数光谱和光谱编码技术,并从地物光谱曲线中提取了其光谱吸收特征.对同类曲线特征求交得到识别地物的有效特征;对不同类曲线特征求交得到区分不同类地物的有效特征.最后基于提取的特征建立了地物识别决策树,从而达到快速识别分类地物的目的,能够实现依据地物光谱特征的地物识别与分类.     

基于经验模分解高光谱图像数据尺度变换的光谱保真性分析

经验模分解是具有自适应性特点的尺度变换方法。高光谱图像数据近乎连续的光谱是开展信息提取的重要信息来源,然而尺度变换会使光谱发生变化。因此,分析EMD尺度变换后高光谱图像数据的光谱保真性具有重要意义。应用CHRIS高光谱图像数据,使用光谱相关系数、光谱偏差、光谱相对偏差和光谱角等评价指标,开展EMD升尺度图像及其典型地物的光谱保真性实验,并将EMD与Mallat小波变换光谱保真性比较。实验结果得出:11~10级EMD尺度变换后图像整体光谱保真性都较好,相关系数均在0.979以上,偏差小于55,相对偏差小于0.036,光谱角在0.041以内;2图像光谱保真性随EMD尺度变换次数增加而略有降低,且前4级变换光谱失真相对明显,后续降幅微弱;31~10级尺度变换后7种湿地典型地物的光谱保真性都较好,其中芦苇和河流的光谱保真程度较突出,养殖水面的相对不理想;4EMD与小波变换光谱保真性比较,随着变换次数的增加EMD表现出相对稳定且较小的光谱失真。     

Image fluctuation model for damage detection using middle‐resolution satellite imagery

A damage detection method using middle‐resolution satellite images, the Image Fluctuation Model method, is proposed, which employs a stochastic model of the digital number (DN) fluctuation in a normal condition and its significance test. The DN fluctuation model is formulated by considering an imaging process of a satellite sensor and an image registration process. A resulting thematic map is created based on a confidence level (1?significance level), which is defined on a pixel‐by‐pixel basis as follows: the minimum significance level at which the null hypothesis, that the pixel DN can be considered as a sample of the DN fluctuation model, is rejected. The confidence level provides the model‐based probability of ground surface change. The method is applied to the 2003 Bam, Iran earthquake using images acquired by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) of Terra. The receiver operating characteristic curves of the method showed better detection performance than temporal image differencing or temporal image ratioing. Though the detection performance of building damage was not comparable to visual inspection on a building basis using high‐resolution images of QuickBird, the confidence level map shows similarity at the district level to damage assessment results using high‐resolution images.     

Change Detection Using Adaptive Fuzzy Neural Networks: Environmental Damage Assessment after the Gulf War

This article introduces an adaptive fuzzy neural network classifier for environmental change detection and classification applied to monitor landcover changes resulting from the Gulf War. In this study, landcover change is treated as a qualitative shift between landcover categories. The Change Detection Adaptive Fuzzy (CDAF) network learns fuzzy membership functions for each landcover class present at the first image date based on a sample of the image data. An image from a later date is then classified using this network to recognize change among familiar classes as well as change to unfamiliar landcover classes. The CDAF network predicts landcover change with 86% accuracy representing an improvement over both a standard multidate K-means technique which performed at 70% accuracy and a hybrid approach using a maximum likelihood classifier (MLC)/K-means which achieved 65% accuracy. In this study, we developed a hybrid classifier based on conventional statistical methods (MLC/K-means classifier) for comparison purposes to help evaluate the performance of the CDAF network. The CDAF compared with existing change detection methodology has two features that lead to significant performance improvements: 1) new landcover types created by a change event automatically lead to the establishment of new landcover categories through an unsupervised learning strategy, and 2) for each pixel the distribution of fuzzy membership values across possible categories are compared to determine whether a significant change has occurred.     

基于奇异值分解的伪光谱图像压缩算法

提出了一种光谱图像的快速获取和有效压缩算法。将一幅三原色图像转换为光谱图像,其像素被通过查找光谱调色板后所确定的相应的光谱反射因子取代,该反射因子的色度值与原像素最为接近。此时转换所得的图像并没有真实光谱数据而只有基于调色板的伪光谱值,它可用于模拟原始RGB图像在不同光源条件下的颜色外观。奇异值分解法对于光谱图像的压缩是非常有用的,人们可以对局部子块图像中经过重新排列后的36个像素×36个光谱波段的数据运用该方法,以能够充分利用矩阵数据在空间和光谱分布中的强相关性。实验结果表明,基于奇异值分解的光谱图像压缩算法在较高的压缩比下仍能取得很好的光谱重建效果。     

The impact of object size on the thematic accuracy of landcover maps

We recently completed the accuracy assessment of a Landsat-derived landcover polygon layer covering the entire province of Alberta (660,000 km²), Canada, for which we gathered reference information for nearly 5000 randomly selected polygons ranging from two hectares to thousands of hectares in size. This gave us the unique opportunity to quantify, for the first time, how the probability of correctly classifying a landcover object varies with its size. Irrespective of whether they are represented as polygons or as sets of connected pixels with the same label, the classification accuracy of landcover objects decreases as their size decreases, steadily for large and medium sizes, and more dramatically when they are within two orders of magnitude of the pixel size of the input image. We show that this size-dependency is bound to occur whenever the size distribution of landcover objects follows an inverse power law. Our results are consistent with previous studies on related issues, confirm the need to account for size when assessing the accuracy of object-based landcover maps, and cast doubts on the validity of (1) recently proposed object-based accuracy estimators, and (2) landscape pattern analyses where the minimum patch size is close to the pixel size.     

On properties of discretized convex curves

The connection between a continuous convex curve and its discrete image is investigated using an appropriate definition of discrete convexity. It is shown that the discrete image of continuous convex curve may not be convex; however, its deviation from convexity (in the discrete definition) is bounded by a small constant. The actual pixel patterns that are obtained by discretizing convex curves are studied. Certain constraints on the context of discrete images of continuous convex curves were discovered     

A Ray Tracing Approach to Diffusion Curves

Diffusion curves [ [OBW*08] ] provide a flexible tool to create smooth‐shaded images from curves defined with colors. The resulting image is typically computed by solving a Poisson equation that diffuses the curve colors to the interior of the image. In this paper we present a new method for solving diffusion curves by using ray tracing. Our approach is analogous to final gathering in global illumination, where the curves define source radiance whose visible contribution will be integrated at a shading pixel to produce a color using stochastic ray tracing. Compared to previous work, the main benefit of our method is that it provides artists with extended flexibility in achieving desired image effects. Specifically, we introduce generalized curve colors called shaders that allow for the seamless integration of diffusion curves with classic 2D graphics including vector graphics (e.g. gradient fills) and raster graphics (e.g. patterns and textures). We also introduce several extended curve attributes to customize the contribution of each curve. In addition, our method allows any pixel in the image to be independently evaluated, without having to solve the entire image globally (as required by a Poisson‐based approach). Finally, we present a GPU‐based implementation that generates solution images at interactive rates, enabling dynamic curve editing. Results show that our method can easily produce a variety of desirable image effects.     

基于计算机视觉的板类零件曲面测量系统

利用计算机视觉原理，建立了板类零件曲面测量系统，该系统首先根据人眼感知事物原理，采用神经网络来拟合图像坐标与空间坐标的映射关系；然后以光栅投影条纹为特征，用小波变换提取条纹边缘，在此基础上，提出搜索式无监督聚类方法，使带状离散边缘点按边缘实际分布情况分为不同组群，并将各组边缘点分别拟合成连续B样条曲线，同时结合视觉几何不变性，实现了亚象素级的立体精匹配；接着，运用小波分解来讲接图象，融合数据，并由图象坐标与空间坐标的映射关系，求解曲面上点的空间坐标，测量精度可控制在0．5mm/m以内。     

不同曝光值图像的直接融合方法

提出了一种直接从同一场景多次不同曝光值下成像的LDR(low dynamic range)图像序列中提取每个像素位置最佳成像信息的图像融合方法,可以在无需任何拍摄相机参数及场景先验信息的情况下,快速合成适合在常规设备上显示的HDR(high dynamic range)图像.该方法利用特殊设计的鲁棒性曲线拟合算法建立LDR图像序列中每个像素位置像素值曲线的数学模型,并由此给出评价单个像素成像时曝光合适程度的标准和融合最佳成像像素信息的方法.对不同场景的大量实验结果显示,该方法的计算结果与传统HDR成像技术经过复杂的HDR重建和色调映射计算后得到的结果相当,但具有更高的计算效率,并同时对图像噪声、相机微小移动和运动目标的影响具有较好的鲁棒性.     

基于特殊点跟踪的线状目标矢量化研究

针对单像素线宽线状目标栅格图像矢量化,提出了一种基于特殊点跟踪的矢量化算法。该算法通过提取曲线端点和曲线间交叉点这两类特殊点,以扫描跟踪的方式提取曲线上的所有像素点坐标,采用Douglas-Peucker法对曲线栅格点进行压缩,获得最终矢量图。实验结果表明,该算法能够有效地实现线状目标栅格图像的矢量化,在选取数据压缩阀值为2个像素的情况下,矢量图与原栅格图的距离差均值小于0.4个像素,数据压缩比大于850。     

层次混合模型快速遥感影像分割算法

针对高分辨率遥感影像中同物异谱和同谱异物导致的分割困难问题,提出一种层次高斯混合模型(HGMM)快速遥感影像分割算法.首先采用HGMM构建影像的统计模型,其具有准确建模像素强度统计分布呈现的非对称、重尾和多峰等复杂特性的能力;然后根据贝叶斯理论构建基于HGMM的分割模型,为了简化参数求解并提高算法效率,定义均值和方差为关于权重的函数;最后采用共轭梯度(CGM)求解模型参数.实验中采用所提出算法和传统统计模型分割算法分别对合成、全色和彩色高分辨率遥感影像进行分割实验.实验结果表明,所提出的HGMM具有准确建模复杂统计分布的能力,且能够准确和有效地分割全色和彩色遥感影像.