基于数学形态学的灰度图像车牌字符提取算法

在深入研究数学形态学的基础上,提取了一种灰度图像车牌字符提取算法。该算法构造4种结构元素,采用数学形态学的膨胀和腐蚀算子依次求出梯度算子对车牌字符进行边缘检测处理和迭代阈值分割,最后结合数学形态学的区域填充方法弥合字符中的空隙。与传统边缘检测算子相比较,实验结果表明该算法具有较强的提取字符能力和良好的抗噪能力,并保护了字符的边缘。其计算量小,在保证处理效果的同时,保证了处理速度,具有一定的可用性和可行性。     

基于像元纯净指数的多端元提取算法

针对传统单端元提取方法不能描述端元变异、限制混合像元分解精度的缺点,提出一种基于像元纯净指数的多端元提取算法(Multiple Endmember Extraction Algorithm Based on Pixel Purity Index,PPI-MEE)。首先将图像划分为不重叠的图像块,并分别利用改进的PPI算法提取候选端元集,然后利用候选端元的邻域像元光谱信息对候选端元进行优化和精选。最后,对优化精选后的端元集分类得到每类地物的多端元光谱集。仿真数据和真实高光谱数据的实验结果表明,提出的多端元提取策略具有表征遥感图像中端元光谱变异的能力,能够提高端元提取精度和混合像元分解精度。     

一种有效的视频文字提取方法

根据文字区域内集中分布着短小的水平垂直边缘,而背景区的边缘则比较粗、比较长的特征,提出一种视频文字提取方法.它使用相关熵二值化和形态学膨胀获得候选文字区,然后提取候选区域的变异直方图,根据该直方图获得精确的文字区.选用不同视频截图实验,结果表明该方法简单,具有较高的正确率、精度和鲁棒性.     

针对高光谱端元提取的空谱联合预处理方法

混合像元的存在是制约高光谱遥感应用精度的主要原因,因此必须进行高光谱解混合。端元提取作为高光谱解混合的关键,往往易受噪声和异常点的干扰。为了提高端元提取精度,针对高光谱端元提取提出了一种空谱联合的预处理方法。首先,定义了新概念光谱纯度指数,主要用于预估高光谱图像中每个像元的光谱纯度;其次,给出了基于光谱纯度指数的空间去冗余方法,利用真实地物的空间分布连续性,判断和移除高光谱图像中冗余像元,最终形成精简的候选端元集。实验结果表明:采用提出的预处理方法后,对于模拟高光谱图像,提取的端元与原始端元之间夹角平均减少了9.022 3°,候选端元数量少于原始像元数量的10%。该预处理方法不仅有效消除了噪声和异常点的干扰,提高了端元提取精度,且大幅降低了时间复杂度。     

基于测地线距离和正交投影的端元提取算法

端元提取是高光谱遥感图像混合像元分解的关键步骤。传统线性端元提取方法忽略了像元内地物的非线性混合因素,制约了混合像元分解精度的提升。针对高光谱图像数据的非线性结构,提出一种基于测地线距离的正交投影端元提取算法,将测地线距离引入端元单体提取过程,利用正交投影方法逐个提取端元。为了降低测地线距离计算量,在端元提取前先利用自动目标生成方法和无约束最小二乘法对原始高光谱数据进行数据约减。模拟和真实高光谱图像实验表明,该方法能够表征光谱数据中非线性因素,端元提取结果优于传统自动目标生成端元提取方法。     

基于自适应形态学滤波的红外小目标检测算法

针对低对比度下小目标常被大量背景杂波和噪声干扰,检测结果不理想的问题,提出了一种基于视觉注意机制与自适应双结构元素形态学滤波的红外小目标检测方法。根据人类视觉对比机制对图像进行感兴趣区域(ROI)提取以确定候选目标,通过提取轮廓获得候选目标的尺寸,并由获取的尺寸自适应构造双结构元素。运用双结构元素形态学滤波抑制噪声和杂波信号,用中值滤波对形态学滤波后的杂点噪声进一步抑制。实验表明本文提出的算法能有效抑制噪声干扰,显著提高目标信杂比,准确检测弱小红外目标,算法具有很好的鲁棒性和实时性。     

路面裂缝识别中边缘检测间断性的改进

针对传统边缘检测算子得到图像的间断和不连续的特点,结合基于形态学的图像膨胀算法和图像细化算法对路面裂缝图像的边缘检测进行了改进。先介绍了几种常用的边缘检测算子,并利用各个算子对路面裂缝图像进行了边缘检测,将结果进行了对比,根据对比结果选出了Soble算子为本次实验所用的边缘检测算子,在其基础上改进。最后,根据该类路面裂缝图像的特点,改用"菱形"结构元素代替传统的"方形"结构元素,将间断的路面裂缝图像边缘处理成连续的清晰的边缘,达到了很好的效果。     

基于多尺度多结构元的彩色形态学边缘检测

针对图像中的几何特征和噪声特性,文章提出了一种基于多尺度多结构元的彩色形态学边缘检测算法。该方法首先在HSL空间定义了多结构元彩色形态学基本算子,在此基础上利用不同尺度的结构元素提取彩色图像边缘,然后用多尺度合并算法对各个边缘进行合成以得到边缘检测结果。经大量的实验证明,多尺度多结构元的彩色形态边缘检测有着比单一尺度结构元的彩色形态边缘检测更优越的性能,在有噪声干扰的情况下,和传统的方法相比,该算法能更好地抑制噪声并且提取更多有用的边缘信息,满足不同的应用需求。     

基于同质区分割的非线性端元提取算法

端元提取是高光谱遥感图像混合像元分解的关键步骤。传统端元提取算法忽略了高光谱图像中地物空间分布相关性与非线性结构,制约了端元提取算法的精度。针对高光谱图像的空间关系与非线性结构,提出一种基于同质区分割的非线性端元提取算法。使用超像素分割方法将图像分割为若干同质区,利用流形学习构造高光谱图像数据的非线性结构,最后在同质区内提取端元并利用聚类方法优选端元。模拟和真实图像数据实验表明,该算法能够保证高光谱数据的非线性结构,端元提取结果优于其他传统线性端元提取方法,在低信噪比的情况下,可以保持较好的端元提取结果。     

基于代数余子式的N-FINDR快速端元提取算法

基于高光谱图像特征空间几何分布的端元提取方法通常可分为投影类算法和单形体体积最大类算法,通常前者精度不好,后者计算复杂度较高。该文提出一种基于代数余子式的快速N-FINDR端元提取算法(FCA),该算法融合了投影类算法速度快和单形体体积最大类算法精度高的优势,利用像元投影到端元矩阵元素的代数余子式构成的向量上的方法,寻找最大体积的单形体。此外,该算法在端元搜索方面较为灵活,每次迭代都可用纯度更高的像元代替已有端元,因此能保证用该端元确定的单形体,可以将特征空间中全部像元包含在内。仿真和实际高光谱数据实验结果表明,该文算法在精准提取出端元的同时,收敛速度非常快。     

A hyperspectral image endmember extraction algorithm based on generalized morphology

Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extraction algorithm based on generalized morphology is proposed. For the limitations of morphological operator in the pixel arrangement rule and replacement criteria, the reference pixel is introduced. In order to avoid the cross substitution phenomenon at the boundary of different object categories in the image, an endmember is extracted by calculating the generalized opening-closing (GOC) operator which uses the modified energy function as a distance measure. The algorithm is verified by using simulated data and real data. Experimental results show that the proposed algorithm can extract endmember automatically without prior knowledge and achieve relatively high extraction accuracy.     

Spatial/spectral endmember extraction by multidimensional morphological operations

Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.     

一种改进的N-FINDR高光谱端元提取算法

光谱端元提取是对高光谱数据进一步分析的重要前提。在各种端元提取算法中,N-FINDR算法因其全自动和选择效果较好等优点受到了广泛的关注。然而样本的排序对该算法的端元提取会造成一定影响,并且传统N-FINDR算法需要根据端元的个数进行降维处理,从而限制了该算法的应用。实际高光谱数据中存在的同一地物在高维空间中非紧密团聚现象也对端元提取增加了难度。为此该文提出改进的算法停机准则和数据特征预处理方法,并使用支持向量机对提取到的端元进行二次提取。实验结果表明,改进的停机准则进一步增加了由端元向量组组成的凸体体积。数据特征预处理和基于支持向量机的二次端元提取分别提升了数据的可分性和提取到端元的精度。     

高光谱遥感图像端元提取的零空间光谱投影算法

端元提取技术是高光谱遥感图像光谱解混的关键.在线性光谱混合分析中,首先引入了高光谱遥感图像经过零空间光谱投影后具有单形体的凸不变性.在此基础上,提出了零空间光谱投影算法,通过设计各种度量和准则,制定不同的单次端元提取策略,灵活地实现算法.经过证明,零空间光谱投影算法是对基于子空间投影距离算法(包括零空间投影距离算法与经典正交子空间投影算法)的进一步延伸,提供了更多的端元提取策略.实验结果表明,零空间光谱投影算法在模拟图像以及真实高光谱遥感图像中都能够有效地提取出图像中的各种端元.     

Iterative Spectral Unmixing for Optimizing Per-Pixel Endmember Sets

Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the endmembers that comprise it are used, with larger errors occurring if inappropriate endmembers are included in the unmixing process. This paper presents an iterative implementation of SMA (ISMA) to determine optimal per-pixel endmember sets from the image endmember set using two steps: 1) an iterative unconstrained unmixing, which removes one endmember per iteration based on minimum abundance and 2) analysis of the root-mean-square error as a function of iteration to locate the critical iteration defining the optimal endmember set. The ISMA was tested using simulated data at various signal-to-noise ratios (SNRs), and the results were compared with those of published unmixing methods. The ISMA method correctly selected the optimal endmember set 96% of the time for SNR of 100 : 1. As a result, per-pixel errors in fractional abundances were lower than for unmixing each pixel using the full endmember set. ISMA was also applied to Airborne Visible/Infrared Imaging Spectrometer hyperspectral data of Cuprite, NV. Results show that the ISMA is effective in obtaining abundance fractions that are physically realistic (sum close to one and nonnegative) and is more effective at selecting endmembers that occur within a pixel as opposed to those that are simply used to improve the goodness of fit of the model but not part of the mixture     

Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization

Endmember extraction is a process to identify the hidden pure source signals from the mixture. In the past decade, numerous algorithms have been proposed to perform this estimation. One commonly used assumption is the presence of pure pixels in the given image scene, which are detected to serve as endmembers. When such pixels are absent, the image is referred to as the highly mixed data, for which these algorithms at best can only return certain data points that are close to the real endmembers. To overcome this problem, we present a novel method without the pure-pixel assumption, referred to as the minimum volume constrained nonnegative matrix factorization (MVC-NMF), for unsupervised endmember extraction from highly mixed image data. Two important facts are exploited: First, the spectral data are nonnegative; second, the simplex volume determined by the endmembers is the minimum among all possible simplexes that circumscribe the data scatter space. The proposed method takes advantage of the fast convergence of NMF schemes, and at the same time eliminates the pure-pixel assumption. The experimental results based on a set of synthetic mixtures and a real image scene demonstrate that the proposed method outperforms several other advanced endmember detection approaches     

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.