Spectral Filtering and Classification of Terrestrial Laser Scanner Point Clouds

A method is presented for filtering and classification of terrestrial laser scanner point clouds. The algorithm exploits the four-channel (blue, green, red and near infrared) multispectral imaging capability of some terrestrial scanners using supervised, parametric classification to assign thematic class labels to all scan cloud points. Its principal advantage is that it is a completely data-driven algorithm and is independent of spatial sampling resolution since the processing is performed in four-dimensional spectral feature space. Its application to two data-sets of different spatial extent and spatial and spectral complexity is reported, for which respective overall classification accuracies of 87·0% and 82·0% were achieved. Analysis of the input data with emphasis on the characteristics pertinent to the anticipated outcomes precedes detailed analysis of the classification results and error sources and their causes. Erroneously classified points are attributed to radiometric errors stemming from both detector hardware and physical effects.     

IMD法在多光谱图像分类中的应用

数学形态学着重于分析和处理图像的几何结构信息。根据这一原理，在图像分类预处理过程中，对各种地物区域的几何结构特点进行分析，构造相应的结构元素，然后对图像进行形态迭代分解法(IMD，Iterative Morphological Decompostion)变换。在变换结果中，地物区域的灰度信息进行了归一化处理，同时区域的几何结构特点和独立地物得到了有效保留。将多光谱图像的IMD变换结果进行分类实验的结果表明，该方法可以有效提高多光谱图像分类的精度和效率，具有较强的适用性。     

Advances in lunar science from the Clementine mission: A decadal perspective

The Clementine spacecraft orbited the Moon and acquired science data for 10 weeks in the Spring of 1994. During this time it collected global 11-band multispectral images and near global altimetry. Select areas of the Moon were imaged at 25 m/pixel in visible light and 60 m/pixel in thermal wavelengths. From these datasets a new paradigm for the evolution of the lunar crust emerged. The Moon is no longer viewed as a two-terrane planet, the Apollo samples were found not to represent the lunar crust as a whole, and the complexity of lunar crustal stratigraphy was further revealed. More than ten years later the Clementine datasets continue to significantly advance lunar science and will continue to do so as new measurements are returned from planned missions such as Chandrayaan, SELENE, and Lunar Reconnaissance Orbiter. This paper highlights the scientific research conducted over the last decade using Clementine data and summarizes the influence of Clementine on our understanding of the Moon.     

利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识

青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法:以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。     

多光谱遥感影像亮度空间相位一致性特征点检测

提出了一种基于亮度空间和相位一致性理论的多光谱遥感影像特征点检测算法。首先利用参数自适应的灰度变换函数建立影像亮度空间;然后结合相位一致性方法在影像亮度空间进行候选特征点检测,并将候选特征点映射到原始影像上进行非极大值抑制;最后在尺度空间计算特征点的特征尺度值。本文方法有效结合了亮度空间特征检测和相位一致性特征检测的优势,对多光谱遥感影像的辐射变化具有较强的稳健性。试验结果证明,与传统特征点检测算法相比,本文方法在特征重复率和重复特征数量方面都具有明显的优势。     

天绘一号03星三线阵/多光谱载荷影像质量评价

2015年10月26号发射的天绘一号03星是中国传输型立体测绘卫星天绘一号系列的第3颗卫星。三线阵和多光谱影像数据是天绘一号卫星的重要数据,用于立体测绘、彩色融合、遥感观测等多种用途。因此,为了推动天绘一号03卫星影像数据早日为相关领域提供服务,本文采用客观评价的方法,对天绘一号03星的三线阵和多光谱影像质量进行了全面的评价分析。评价结果显示(天绘一号)03星的清晰度、对比度、细节能量、边缘能量、功率谱、信息容量指标值远高于天绘一号01、02星。说明天绘一号03星三线阵/多光谱影像在地物细节和边缘纹理特征的描述上优于天绘一号01、02星,同时载荷接收的信息量远高于天绘一号01、02星。天绘一号03星较高的信噪比指标说明其载荷抑制噪声的能力优于天绘一号01、02星。边缘辐射畸变和增益调整畸变表明在辐射均匀程度上,天绘一号03星三线阵/多光谱影像介于天绘一号01和02星之间。因此,与同类天绘一号01,02星的三线阵与多光谱影像相比,天绘一号03星影像质量有着显著的提高。     

Use of SPOT 5 and IKONOS imagery for mapping biocenoses in a Tunisian Coastal Lagoon (Mediterranean Sea)

Mapping marine biocenoses is an efficient method for providing useful data for the management and conservation of Mediterranean lagoons. Fused images from two satellites, SPOT 5 and IKONOS, were tested as management tools for identifying specific ecosystems in the El Bibane lagoon, situated in southern Tunisia near the Libyan border. The objectives of this study were to provide a precise map of the entire El Bibane lagoon using fused images from SPOT 5 and to compare fused images from SPOT 5 and IKONOS over a test-area. After applying a supervised classification, pixels are automatically classified in four classes: low seagrass cover, high seagrass cover, superficial mobile sediments and deep mobile sediments. The maps of the lagoon revealed and confirmed an extremely wide distribution of seagrass meadows within the lagoon (essentially Cymodocea nodosa; 19 546 ha) and a large area of mobile sediments more or less parallel to the shore (3 697 ha). A direct comparison of overall accuracy between SPOT 5 over the entire area, SPOT 5 over the test-area and IKONOS over the test-area revealed that these tools provided accurate mapping of the lagoon environment (83.25%, 85.91% and 73.41% accuracy, respectively). The SPOT 5 images provided greater overall accuracy than the IKONOS image, but did not take into account the heterogeneous spatial structure of the seagrasses and sediments present in the lagoon environment. Although IKONOS imagery provided lower overall accuracy than SPOT 5, it proved a very useful tool for the mapping of heterogeneous structures as it enabled the patchiness of formations to be better taken into account. The use of SPOT 5 and IKONOS fused images appears to be very promising for completing the mapping of lagoons in other regions and countries of the Mediterranean Sea.     

机载多光谱LiDAR的随机森林地物分类

机载多光谱LiDAR技术利用激光进行探测和测距,不仅可以快速获取地面物体的三维坐标,还可以获得多个波段的地物光谱信息,可广泛用于地形测绘、土地覆盖分类、环境建模、森林资源调查等。本文提出了多光谱LiDAR的随机森林地物分类方法。该方法通过对LiDAR强度数据和高程数据提取分类特征,完成多光谱LiDAR的随机森林地物分类;并分析随机森林的特征贡献度特性,采用后向特征选择方法实现分类特征选择。通过对加拿大Optech Titan多光谱LiDAR数据的试验表明：随机森林方法可以获得较好的地物分类精度,而且可以适当地去除部分冗余和相关的特征,从而有效提高分类精度。     

基于无人机的用海项目施工期悬浮泥沙监测

为实现对用海项目施工期悬浮泥沙扩散的实时跟踪监测,尝试采用无人机携带可见光、多光谱传感器对古雷围填海项目的围堰区进行遥感探测。利用Pix4d软件对无人机携带的可见光及多光谱照片进行处理,结果表明,可见光及多光谱影像对水体中的悬浮泥沙分布均具有一定的识别能力。通过研究分析多光谱影像的光谱特征发现,近红外波段对悬浮泥沙含量的升高较为敏感,运用波段比值公式计算结果显示,围堰内含沙水体经溢流口排入海域后浓度明显降低,并可向南继续扩散约300 m。该方法可克服遥感卫星影像时空分辨率低的特点,为用海项目资源环境影响跟踪监测提供新的技术手段。     

基于无人机和MODIS数据的巴丹吉林沙漠植被分布特征与动态变化研究

荒漠植被在减缓沙漠扩张、阻挡风沙入侵绿洲、维护绿洲稳定性方面具有重要意义。通过无人机搭载多光谱相机获取地面样地高分辨率NDVI数据以计算植被覆盖度,结合MODIS-NDVI数据,分析了MODIS-NDVI与无人机获取NDVI数据之间的一致性,进而建立MODIS-NDVI数据与样方植被覆盖度的空间反演模型,以研究巴丹吉林沙漠植被分布特征、动态变化及其对气温、降水的响应规律。结果表明:(1)基于无人机多光谱数据大样方植被覆盖度和MODIS-NDVI数据构建的植被覆盖度反演模型具有较高的模拟精度。(2)研究区总体植被覆盖度较低,空间上呈由东南向西北递减的趋势。(3)植被覆盖度总体呈现上升趋势,沙漠边缘的增速较沙漠内部更为明显,其中沙漠边缘增长率达1.53%/10a,而沙漠内部为0.84%/10a。沙漠边缘57.12%植被出现改善,而沙漠内部植被改善的面积约为21.26%。(4)植被生长主要受制于生长季降水量并呈现出明显的季节变化,此外植被对降水和气温的响应存在一定的滞后效应。