星载超光谱成像仪摆镜系统轴系的误差分析与设计

摆镜系统轴系的精度会影响星载超光谱成像仪的成像质量。轴系设计时采用了向心推力球轴承来调整轴系的装配误差。分析了摆镜系统工作时影响轴系精度的几个误差源,包括零件的尺寸误差、同轴度误差、配合误差等,按照误差理论设计并计算了各误差值,得到轴系的径向晃动误差和角运动误差。对加工好的轴系进行精度检测,实验结果符合理论结果,验证了该摆镜系统的轴系结构设计的合理性和可靠性,为该类航天运动部件的轴系结构设计提供了参考依据。     

Multivariate image analysis: A review with applications

Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction.     

基于协峭度张量的高光谱图像异常检测

高光谱图像中的异常像元往往具有在图像中出现的概率低和游离于背景数据云团之外的特点,如何“自动”确定这些异常像元是高光谱遥感图像处理中的一个重要研究方向。经典的高光谱异常检测方法一般从图像的统计特性入手,广泛应用的RXD异常检测算法通过计算图像的2阶统计特征,可以直接给出异常点的分布情况,算法复杂度低,但缺点是没有考虑到图像的高阶统计信息。基于独立成分分析的异常检测算法虽然考虑了高阶统计量对异常点的敏感性,但需要反复迭代提取异常成分后,再对提取后的成分进行异常检测。该文提出一种基于协峭度张量的异常检测算法,该算法不需要事先提取异常成分,可以直接对观测像元进行逐一检测,从而给出异常点的分布情况。基于模拟数据和真实数据的实验结果表明,该方法能够在检测出异常像元的同时更好地压制背景信息、减小虚警率,从而提高异常检测精度。

     

基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩

针对基于预测的高光谱图像无损压缩算法压缩比低的问题,该文将聚类算法与高光谱图像预测压缩算法相结合,提出一种基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩算法。首先,对高光谱图像按光谱矢量进行K-均值聚类以提升同类光谱矢量间的相似度。然后,对每一聚类群分别使用传统递归最小二乘法进行预测,消除高光谱图像的空间冗余和谱间冗余。最后,对预测误差图像进行算术编码,完成高光谱图像压缩过程。对AVIRIS 2006高光谱数据进行仿真实验,所提算法对16位校正图像、16位未校正图像和12位未校正图像分别取得了4.63倍,2.82倍和4.77倍的压缩比,优于同类型已报道的各种算法。     

高光谱图像分类的融合分层深度网络联合稀疏表示算法

在高光谱图像分类领域中每个像素的局部邻域一旦包含来自不同类别的样本,联合稀疏表示将受邻域内字典原子与测试样本之间同谱异类的影响,严重降低分类性能.根据高光谱图像的特点,文中提出融合分层深度网络的联合稀疏表示算法.在光谱和空间特征学习之间交替提取判别性光谱信息和空间信息,构建兼具空谱特征的学习字典,用于联合稀疏表示.在分类过程中将学习字典与测试样本间的相关系数与分类误差融合并决策.在两个高光谱遥感数据集上的实验验证文中算法的有效性.     

基于图稀疏正则化多测量向量模型的高光谱压缩感知重建

压缩感知重建是解决高光谱现有成像模式数据量大冗余度高问题的一个有效机制。针对高光谱图像的多通道特性,该文建立了高光谱压缩感知的多测量向量模型,编码端使用随机卷积算子对各通道进行快速采样,生成测量向量矩阵。解码端构建图稀疏正则化的联合重建模型,在稀疏变换域将高光谱图像分解为谱间的关联成分和差异成分,通过图结构化稀疏度量表征关联成分的空谱相关性,并约束谱间差异成分的稀疏性。进一步提出模型求解的交替方向乘子迭代算法,通过引入辅助变量与线性化技巧,使得每一子问题均存在解析解,降低了模型求解的复杂度。对多个实测数据集进行了对比实验,实验结果验证了该文模型与算法的有效性。     

基于稀疏表示的高光谱数据压缩算法

如何降低高光谱图像大规模数据的存储和传输代价一直是学者们关心的问题。该文提出一种基于稀疏表示的高光谱数据压缩算法,通过一种波段选择算法构造训练样本集合,利用训练得到的基函数字典对高光谱数据所有波段进行稀疏编码,并对表示结果中非零元素的位置和数值进行量化和熵编码,从而实现高光谱图像压缩。实验结果表明该文算法与3维小波相比具有更好的非线性逼近性能,其率失真性能明显优于3D-SPIHT,并且在光谱信息保留上具有巨大的优势。     

基于空谱联合的多假设预测高光谱图像压缩感知重构算法

为充分利用高光谱图像的空间相关性和谱间相关性,该文提出一种基于空谱联合的多假设预测压缩感知重构算法。将高光谱图像分组为参考波段图像和非参考波段图像,参考波段图像利用光滑Landweber投影算法重构,对于非参考波段图像,引入空谱联合的多假设预测模型,提高重构精度。非参考波段图像中每个图像块的预测值不仅来自非参考波段图像未经预测的初始重构值的相邻图像块,而且来自参考波段重构图像相应位置及其邻近的图像块,利用预测值得到测量域中的残差,然后对残差进行重构并对预测值进行修正,此残差比原图像更稀疏,且算法采用迭代方式提高重构图像的精度。借助Tikhonov正则化方法求解多假设预测的权重系数,并基于结构相似性判断是否改变多假设预测搜索窗口大小,最后利用交叉验证计算重构算法终止迭代的判据参数。实验结果表明,所提算法优于仅利用空间相关性或谱间相关性进行预测和不预测的重构算法,其重构图像的峰值信噪比提高2 dB以上。     

Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada

To effectively manage forested ecosystems an accurate characterization of species distribution is required. In this study we assess the utility of hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada. Using hyperspectral imagery yielded producer's and user's accuracies for most species ranging from > 52-95.4 and > 63-87.8%, respectively. For species dominated by definable growth stages, pixel-level fusion of hyperspectral imagery with LiDAR-derived height and volumetric canopy profile data increased both producer's (+ 5.1-11.6%) and user's (+ 8.4-18.8%) accuracies. McNemar's tests confirmed that improvements in overall accuracies associated with the inclusion of LiDAR-derived structural information were statistically significant (p < 0.05). This methodology establishes a specific framework for mapping key species with greater detail and accuracy then is possible using conventional approaches (i.e., aerial photograph interpretation), or either technology on its own. Furthermore, in the study area, acquisition and processing costs were lower than a conventional aerial photograph interpretation campaign, making hyperspectral/LiDAR fusion a viable replacement technology.