基于属性滤波和上下文分析的高分辨遥感图像建筑物提取方法

遥感图像分辨率的提高在带来丰富细节的同时,也给传统的目标提取方法提出了新的挑战。该文根据建筑物目标的属性和上下文关系,提出了一种新的高分辨率遥感图像中建筑物目标提取方法。该方法首先以具有相似属性的连通像素为处理单元,依据地物对象间的属性差异,对遥感图像进行不同尺度的属性滤波;接着根据建筑物和阴影的自身属性特点,对上述属性滤波结果进行粗筛,形成备选目标集;最后,进一步考虑建筑物与其阴影间的上下文关系,从二者的方位角、相对位置和区域距离3方面对备选目标集进行筛选,完成建筑物提取。对居民区和工业区遥感图像的实验结果表明,该方法优于传统方法,可用于提取多种类型的建筑物。

基于级联与组合属性形态学滤波的模糊边界目标识别

应用属性形态学的属性准则级联与组合处理方法对模糊边界目标图像进行滤波,研究面积、面积亮度对比、形状、拓扑高度和后代数等属性形态学滤波原理及滤波适应性。通过最大树节点属性计数方式对模糊边界细胞进行精确统计,利用基于连通域灰度层次的拓扑分布节点属性描述和修剪方法,实现模糊边界分割和粘连目标识别,并采用柔性结构元素对复杂边界图像进行规约性滤波。实验结果表明,与现有目标识别方法相比,该方法能有效排除模糊目标图像中非感兴趣区域的背景干扰,提高对各类细胞的识别精度。

基于面积稳定性与形状属性的杆状区域分割

针对不同光学环境下模糊图像和复杂形状交叠图像中目标区域的分割问题,研究了基于属性形态学分析的杆状区域的滤波和提取方法。应用连通域的面积稳定性和形状属性特征对模糊边界的杆状目标进行滤波,连通域变换中的面积稳定性有效控制粘连细胞等大区域的形成,而形状属性以个体模板目标有效规约连通域的生长和修剪。实验表明:组合属性形态学方法有效地排除了非杆状物等背景干扰,有效分割出的目标像素达90%左右,识别出的目标区域达96%。该方法有效提高了用连通域表示的特定形状区域的分割精度和鲁棒性。

Ground roll attenuation using a time-frequency dependent polarization filter based on the S transform

The ground roll and body wave usually show significant differences in arrival time, frequency content, and polarization characteristics, and conventional polarization filters that operate in either the time or frequency domain cannot consider all these elements. Therefore, we have developed a time-frequency dependent polarization filter based on the S transform to attenuate the ground roll in seismic records. Our approach adopts the complex coefficients of the S transform of the multi-component seismic data to estimate the local polarization attributes and utilizes the estimated attributes to construct the filter function. In this study, we select the S transform to design this polarization filter because its scalable window length can ensure the same number of cycles of a Fourier sinusoid, thereby rendering more precise estimation of local polarization attributes. The results of applying our approach in synthetic and real data examples demonstrate that the proposed polarization filter can effectively attenuate the ground roll and successfully preserve the body wave.

三角测距激光雷达的光斑定位算法研究

为了降低激光雷达测距系统的光斑信号噪声,提高系统的测距精度,对属性距离加权平均滤波及灰度质心法进行改进,提出一种应用于激光雷达系统的改进算法。以曲率为属性的属性距离加权平均滤波在去噪的同时很好地突出了光斑的峰值点。通过多次迭代计算灰度质心的求质心方法,进一步地提高光斑中心的定位精度,还解决了一般平方加权质心法容易受到噪声影响的问题。在MATLAB仿真环境中对实际采集的数据进行处理,实验表明本文算法对于4μs的像素单元可以取得0.05 pix的定位精度,并具有较好的稳定性。

高分辨率SAR影像形态学层级分析的建筑物检测

目的现有基于结构分析的高分辨率SAR影像建筑物检测方法,只考虑了直线和L形结构建筑物,并且依赖建筑物高亮线条处阴影区作为建筑物识别的主要特征;当处于复杂场景时,阴影区受制于背景较暗或建筑物密集而无法准确得到,导致建筑物检测误差大、检测率低。针对上述问题,提出一种基于形态学层级分析的高分辨率SAR影像无监督建筑物检测算法。方法该方法基于单幅单极化高分辨率SAR影像,首先利用改进的形态学交替滤波算子有效抑制其固有的斑点噪声,大大剔除了同质区背景噪声的干扰;然后利用层级分析形态学差分属性断面算法来实现对SAR影像建筑物的几何结构特征的提取;最后结合特征融合和属性阈值分割等后处理步骤得到复杂场景下建筑物提取信息。结果将上述方法在建筑物密集的城区SAR影像中实验,通过与其他方法对比分析,具有检测率高、误差小的特点,准确率和召回率分别为95.38%、86.31%,并对降低虚警率方面有明显的优势。结论将形态学交替滤波与形态学属性滤波的改进与结合,在对不同走向、尺寸和形状的高密度建筑物检测中具有较好的适应性。

Properties of Defect Modes in Periodic Lossy Multilayer with Negative-Index-Materials

Employing the characteristic matrix method, this study investigates transmission properties of onedimensional defective lossy photonic crystals composed of negative and positive refractive index layers with one lossless defect layer at the center of the crystal. The results of the study show that as the refractive index and thickness of the defect layer increase, the frequency of the defect mode decreases. In addition, the study shows that the frequency of the defect mode is sensitive to the incidence angle, polarization, and physical properties of the defect layer, but it is insensitive to the small lattice loss factor. The peak of the defect mode is very sensitive to the loss factor, incidence angle, polarization, refractive index, and thickness of the defect layer. This study also shows that the peak and the width of the defect mode are affected by the numbers of the lattice period and the loss factor. The results can lead to designing new types of narrow filter structures and other optical devices.