点云数据滤波是机载激光雷达(LiDAR)数据处理研究中很重要的问题之一。提出了一种基于区域预测的LiDAR 点云数据形态学滤波算法，该算法由点云数据建立规则格网、去除粗差点，对实验区域进行分块，然后使用各个分块区域的高程标准差预测地形坡度参数s 从而进行渐进式形态学滤波，最终确定地面点。文中算法优点在于可以根据区域地形起伏情况自适应地得到阈值进行滤波。使用国际摄影测量与遥感学会(ISPRS)提供的测试数据进行实验，实验结果表明，提出的算法能够有效去除地物点和保留地面点，并且能有效降低总误差。

给出了一种三角形形变量的定义，并提出了基于不规则三角网(TIN)的LiDAR数据边缘检测新方法。将点LiDAR数据进行三角剖分，生成不规则三角网，计算TIN中每个三角形的形变量，根据三角形形变量的不同来确定处于地物目标边缘的三角形，对这些边缘三角形进行处理得到边缘点。针对LiDAR数据中可能由于河流等导致的数据空白区域，仅利用三角形形变量无法检测到所有边缘点的问题，提出了顶点到重心距离的平方和作为测度来确定狭长三角形，从而提取到河流等数据空白区域的边缘点。实验结果表明，该算法能够较好地提取LiDAR 数据的边缘点，得到LiDAR数据的边缘信息。

Shearlets not only possess all properties that other transforms have, but also are equipped with a rich mathematical structure similar to wavelets, which are associated to a multi-resolution analysis. Recently, shearlets have been used in image denoising, sparse image representation, and edge detection. However, its application in image fusion is still under study. In this letter, we study the feasibility of image fusion using shearlets. Fusion rules of larger high-frequency coefficients based on regional energy, regional variance, and absolute value are proposed because shearlet transform can catch detailed information in any scale and any direction. The fusion accuracy is also further improved by a region consistency check. Several different experiments are adopted to prove that fusion results based on shearlet transform can acquire better fusion quality than any other method.

提出了一种基于多阈值分割和无下采样Contourlet变换(Nonsubsampled Contourlet Transform, NSCT)的多光谱与高分辨率图像融合算法。对多光谱图像进行多阈值分割, 并利用提出的区域均值比指标将多光谱图像划分为需要进行空间细节增强及需要保持光谱特征的区域; 然后利用NSCT对高分辨率图像和多光谱图像的强度分量进行多尺度、多方向分解。分解后的低频部分采用基于窗口邻域的融合规则和算子进行融合, 高频部分按区域均值比指标进行区域融合; 最后进行重构得到融合后的多光谱图像的强度分量, 经IHS逆变换后得到高分辨率的多光谱图像。实验结果表明, 该算法可获得较理想的融合图像, 融合效果优于IHS变换法、基于像素的à trous小波变换法以及基于像素的NSCT法。

提出了一种基于区域分割和Contourlet变换的图像融合算法。首先,对各源图像做区域分割,并利用区域能量比和区域清晰比的概念来度量和提取区域信息;然后,对各源图像进行多尺度非子采样Contourlet分解,分解后的高频部分采用绝对值取大算子进行融合,低频部分则采用基于区域的融合规则和算子进行融合;最后进行重构得到融合图像。对红外与可见光图像进行了融合实验,并与基于像素的à trous小波变换和Contourlet变换的融合效果进行了比较。结果表明,采用本文算法的融合图像既保留了可见光图像的光谱信息,又继承了红外图像的目标信息,其熵值高于基于像素的融合方法约10％,交叉熵仅为基于像素的融合方法的1％左右。

A novel image fusion algorithm based on wavelet-based contourlet transform (WBCT) and principal component analysis (PCA) is proposed. The PCA method is adopted for the low-frequency components. Using the proposed algorithm to choose the greater of the active measures, the region consistency test is performed for the high-frequency components. Experiments show that the proposed method works better in preserving the edge and texture information than wavelet transform method and Laplacian pyramid (LP) method do in image fusion. Four indicators for the fusion image are given to compare the proposed method with other methods.