基于光谱空间变换的遥感图像目标探测方法研究

针对遥感图像在频率域中的表征,提出了一种基于光谱空间变换的遥感图像目标探测方法。该方法首先利用傅里叶变换,将遥感图像从空域转变到频率域;然后利用频谱能量楔状采样和谐波叠置等手段,将不同频谱能量所表征的目标特征信息分解到不同的高、低频段中,由此获取对应目标特征在频率域中的探测标志;最后结合在频谱能量上具有方向和频带选择性的匹配Gabor滤波器,实现了居民楼地物目标的有效探测。试验结果表明,文章所提出的方法能够较好地探测遥感图像的目标信息,并且具有特定方向上目标检测的能力。     

A semi-automated hybrid algorithm for accurate detection and counting of chemically etched tracks

In this paper, we present an accurate and comprehensive semi-automated algorithm for detection and counting the chemically etched tracks on polycarbonate nuclear Track detectors. The proposed algorithm consists of two main phases: a preprocessing phase for image preparation and quality improvement. It uses a format conversion into gray-scale image and contrast enhancement procedure. The contrast enhancement procedure employs the first-order fuzzy moment of the image and the Sugeno class of fuzzy complements to maximize the parametric indices of fuzziness of the image. In one feature extraction phase, two intrinsic characteristics of tracks are defined and formulated as topological feature (TF) and chromatic feature (CF). TF refers to tracks shape and geometry which are characterized and quantized by calculating the local Gray Level Spatial Correlation histogram of the tracks and CF refers to the color intensity difference of the tracks borders and tracks centric areas. The CF characterizes this difference specified to the tracks. After the feature extraction phase, the algorithm enters the detection and counting phase in which the fuzzy sets of background, noise (including small objects, notches, etc.) and track are constructed. Detected objects are then characterized according to their membership values to the fuzzy sets. The algorithm is designed in order to be applicable for different forms of tracks and track counting systems. Accuracy, linearity, sensitivity to the input parameters and comprehensibility of the method are validated in the simulation section.     

A track association algorithm based on the weighted association graph for laser triangulation sensors

The track association problem is a challenging task in dense target environments due to the existence of furcation tracks, cross tracks and combination tracks. At present, most of the track association algorithms experience a substantial decline in accuracy in processing the above three types of tracks. In response to the defect, a track association algorithm based on the weighted association graph is presented. Information divergence is introduced to construct the weighted association graph. And association quality is acquired based on the weighted idea to reflect the association relationship among tracks. The algorithm solves the association problem with furcation tracks, cross tracks and combination tracks effectively, and improves the performance of association algorithms in dense target environments. The simulation results show the effectiveness and the superiority of the algorithm.     

一种新的空谱联合探测高光谱影像目标探测算法

高光谱遥感影像不但具有高分辨率的空间信息还包含连续的光谱信息,因此在目标探测领域具有独特的应用优势。传统的高光谱遥感影像目标探测侧重于光谱信息的应用,形成了确定性算法和统计学算法。确定性算法通过计算目标光谱与待检测光谱之间的距离来查找目标,不能检测亚像素目标,而且容易受到噪声的影响;统计学目标检测计算背景统计特性,通过探测异常点来检测目标,可以检测亚像素目标和小目标,但容易受到目标尺寸的影响,不能很好的检测大目标。随着高光谱遥感影像的空间分辨率的增加,探测目标已有亚像素目标逐步转换为单像素及多像素目标,此时,在高光谱图像中,相同类别的地物在空间分布上呈现聚类特性, 因此,在利用高光谱遥感影像进行目标探测时,需要将其空间信息融入算法中。将空间特征引入传统目标探测算法。提出了一种新的空谱结合的高光谱目标探测算法,将传统的基于统计的目标探测算子与空域邻域聚类算法相结合,首先利用目标探测算子将影像划分为潜在目标区域与背景区域;通过计算潜在目标区域的质心,以质心为中心进行邻域聚类,剔除潜在目标区域中的背景区域,通过迭代计算获取最终目标探测结果。传统的基于统计的目标探测算子,将整个探测区域定义为背景区域,实现对背景区域的统计特征提取,而该方法将背景区域与潜在目标区域分离,剔除了目标区域对背景区域的统计干扰。将本算子与传统的约束能量最小化算子和自适应余弦探测算子进行分析比较可知,该算子的大目标探测性能优于传统的统计算子。     

Application of a cellular automaton for the evolution of etched nuclear tracks

In the present work, it is demonstrated the first application of cellular automata to the growing of etched nuclear tracks. The simplest case in which conical etched tracks are gradually formed is presented, as well as a general case of time varying etching rate V_t. It is demonstrated that the cellular automata elements consist in an image pattern of the latent nuclear track input cells, 16 rules for updating states, the Moore neighborhood and an algorithm of four states.     

适合于生物图像的图像融合算法研究

图像融合作为一种有效的信息融合的技术,已广泛用于军事、遥感、机器视觉和医学图像等领域。本文讨论了三种基于像素级的图像融合算法：Ｐ加权平均,Ｔｏｅｔ算法和基于小波变换的算法;采用四种评价融合效果的量化判据：标准偏差,平均误差,峰值信噪比（ｓ／Ｎ）ｐ和熵差,将三种图像融合算法用于生物图像中细胞荧光图像和透射图像的融合,量化评价结果和视觉判断均说明,对于以细胞荧光图像和透射图像研究对象,需突出荧光图像的     

一种基于小波变换的多尺度多算子图像融合方法

图像融合技术以其综合多传感器信息的优越性日益受到诸多领域的重视。为了使其应用在医学、遥感、计算机视觉、气象预报、军事目标识别等方面更迅速、深入地开展,有效、实用的融合算法是至关重要的。本文在小波变换金字塔结构的基础上,提出了一种多尺度多算子融合方法,对热红外图像和可见光图像的融合进行了研究。结果融合效果很好,目标和背景区别显著,而且边缘不突兀。由于这种方法对小波分解的层数要求不高,因此计算量不大,便于并行处理及硬件实时化实现,具有广阔的应用前景。     

基于单景遥感影像的去云处理研究

去云处理是遥感图像处理以及大气纠正的重要步骤。常规的去云处理算法会随云的覆盖类型的不同而不同,如同态滤波或时间平均法,这些算法在去除云对影像影响的同时,往往会伴随地物信息的丢失。提出了基于单景遥感影像的去云处理算法———基于遥感影像分类结果及云检测结果的去云处理算法,目的是去除影像中云的散射影响,恢复地物的光谱信息。算法是针对局部有云的单景Landsat7 ETM+影像进行的。根据Landsat7 ETM+波段4,5,7对影像进行聚类分析,确定不同地物的覆盖类型;利用波段1,2,3及波段6划分出影像中的无云区以及不同覆盖厚度的云层;按照相同地物覆盖类型对非云区与不同云区进行平均反射率匹配,以达到去云的效果。结果表明,经过去云处理的影像,在分类运算中能够明显地提高分类精度,能够很好地恢复地物的光谱信息。     

污染气体扫描成像红外被动遥测技术研究

傅里叶变换红外光谱技术可实现大气中污染气体的远程被动遥测。而污染气团的位置和分布,对于有害气体泄露情况下泄露位置勘查和污染扩散态势的评估来说是至关重要的。据此设计了一套红外被动成像遥测系统,系统由傅里叶变换光谱仪、数据采集处理软件、扫描系统、可见光成像系统和计算机组成。以六氟化硫为示踪气体,开展了遥测验证实验。通过非线性最小二乘算法结合辐射传输计算反演获得了目标气体的柱浓度,并用伪彩色图显示其分布情况,最后以可见光图像叠加伪彩色柱浓度分布图的形式显示结果。实验分析表明,该系统具有高探测灵敏度,并以可视化和定量的方式显示污染的空间分布情况。     

一种稳健的道路主方向提取算法

基于高分辨遥感影像下直线型道路段的形状特征，提出了一种稳健的道路主方向提取算法．该算法基于结构张量计算的方向直方图探测初始主方向，并根据矢量化的边缘计算精确的方向角，使得探测过程稳健、结果准确．在此基础上可以发展交互式的直线道路段或道路网提取算法．实验结果表明，该算法的结果为进一步的道路边线的准确提取提供了保障．