Multi—beam Sonar and Side—scan Sonar Image Co—registering and Fusing

Multi-beam Sonar and Side-scan Sonar compensate each other.In order to fully utilize all information,it is necessary to fuse two kinds of image and data.And the image co-registration is an important and complicated job before fusion.This paper suggests combining bathymetric data with intensity image,obtaining the characteristic points through the minimal angles of lines,and then deciding the corresponding image points by the maximal correlate coefficient in searching space.Finally,the second order polynomial is applied to the deformation model.After the images have been co-registered,Wavelet is used to fuse the images.it is shown that this algorthm can be used in the flat seafloor or the isotropic seabed.Verification is made in the paper with the observed data.     

多源遥感影像融合及其关键技术探讨

简述了多源遥感影像融合过程、三种融合层次及其优缺点；具体描述了常用的八种融合方法；根据近来多源遥感影像融合研究状况，提出了融合中需进一步研究的关键技术。     

Urban flood modelling combining top-view LiDAR data with ground-view SfM observations

Remote Sensing technologies are capable of providing high-resolution spatial data needed to set up advanced flood simulation models. Amongst them, aerial Light Detection and Ranging (LiDAR) surveys or Airborne Laser Scanner (ALS) systems have long been used to provide digital topographic maps. Nowadays, Remote Sensing data are commonly used to create Digital Terrain Models (DTMs) for detailed urban-flood modelling. However, the difficulty of relying on top-view LiDAR data only is that it cannot detect whether passages for floodwaters are hidden underneath vegetated areas or beneath overarching structures such as roads, railroads, and bridges. Such (hidden) small urban features can play an important role in urban flood propagation. In this paper, a complex urban area of Kuala Lumpur, Malaysia was chosen as a study area to simulate the extreme flooding event that occurred in 2003. Three different DTMs were generated and used as input for a two-dimensional (2D) urban flood model. A top-view LiDAR approach was used to create two DTMs: (i) a standard LiDAR-DTM and (ii) a Filtered LiDAR-DTM taking into account specific ground-view features. In addition, a Structure from Motion (SfM) approach was used to detect hidden urban features from a sequence of ground-view images; these ground-view SfM data were then combined with top-view Filtered LiDAR data to create (iii) a novel Multidimensional Fusion of Views-Digital Terrain Model (MFV-DTM). These DTMs were then used as a basis for the 2D urban flood model. The resulting dynamic flood maps are compared with observations at six measurement locations. It was found that when applying only top-view DTMs as input data, the flood simulation results appear to have mismatches in both floodwater depths and flood propagation patterns. In contrast, when employing the top-ground-view fusion approach (MFV-DTM), the results not only show a good agreement in floodwater depth, but also simulate more correctly the floodwater dynamics around small urban feature. Overall, the new multi-view approach of combining top-view LiDAR data with ground-view SfM observations shows a good potential for creating an accurate digital terrain map which can be then used as an input for a numerical urban flood model.     

Mineralogical alteration of artificial meteorites during atmospheric entry. The STONE-5 experiment

The generic concept of the artificial meteorite experiment STONE is to fix rock samples bearing microorganisms on the heat shield of a recoverable space capsule and to study their modifications during atmospheric re-entry. The STONE-5 experiment was performed mainly to answer astrobiological questions. The rock samples mounted on the heat shield were used (i) as a carrier for microorganisms and (ii) as internal control to verify whether physical conditions during atmospheric re-entry were comparable to those experienced by “real” meteorites. Samples of dolerite (an igneous rock), sandstone (a sedimentary rock), and gneiss impactite from Haughton Crater carrying endolithic cyanobacteria were fixed to the heat shield of the unmanned recoverable capsule FOTON-M2. Holes drilled on the back side of each rock sample were loaded with bacterial and fungal spores and with dried vegetative cryptoendoliths. The front of the gneissic sample was also soaked with cryptoendoliths.

The mineralogical differences between pre- and post-flight samples are detailed. Despite intense ablation resulting in deeply eroded samples, all rocks in part survived atmospheric re-entry. Temperatures attained during re-entry were high enough to melt dolerite, silica, and the gneiss impactite sample. The formation of fusion crusts in STONE-5 was a real novelty and strengthens the link with real meteorites. The exposed part of the dolerite is covered by a fusion crust consisting of silicate glass formed from the rock sample with an admixture of holder material (silica). Compositionally, the fusion crust varies from silica-rich areas (undissolved silica fibres of the holder material) to areas whose composition is “basaltic”. Likewise, the fusion crust on the exposed gneiss surface was formed from gneiss with an admixture of holder material. The corresponding composition of the fusion crust varies from silica-rich areas to areas with “gneiss” composition (main component potassium-rich feldspar). The sandstone sample was retrieved intact and did not develop a fusion crust. Thermal decomposition of the calcite matrix followed by disintegration and liberation of the silicate grains prevented the formation of a melt.

Furthermore, the non-exposed surface of all samples experienced strong thermal alterations. Hot gases released during ablation pervaded the empty space between sample and sample holder leading to intense local heating. The intense heating below the protective sample holder led to surface melting of the dolerite rock and to the formation of calcium-silicate rims on quartz grains in the sandstone sample.     

一种DLG数据要素融合算法的设计与实现

空间数据生产是分幅进行的,因此,在实际应用中经常需要将跨图幅的面状或线状要素,合并成同一要素,使其符合现实世界情况.从数据生产与应用的角度,提出了一种DLG数据要素融合算法,并基于.net平台使用C#语言对算法进行了实现.实践证明,该算法对DLG数据要素融合是有效的,且算法处理效率较高.     

摄影地形测量数据的三维融合和可视化

本文首先解释T-共线方程的概念,提出了基于T-共线方程进行三维配准的准则和数学模型,以及两共轭影像在三维空间进行配准和三维重建的计算方法。可视化将人类对现实世界认识的准则:感觉-思维-认知,应用于对数据世界的认识,因此配准、融合和立体视觉重建,有助于对地球影像的理解和解译。本文以三个案例的卫星和航空摄影地形测量数据,基于T-共线方程,对摄影地形测量数据的配准、融合和可视化,进行了原理性试验。     

基于多特征融合自编码器的无监督地震相分类研究

地震相分类是地震数据解释中的一个重要步骤, 是地震数据与沉积相的连接工具.为了提高地震相分类精度和减少对有限人工标签的依赖, 本文提出了一种基于多特征融合自编码器的无监督地震相分类方法.首先, 提出了一种混合卷积和变分编码的多特征融合自编码器, 实现了地震数据中表征地震相的大量隐含特征提取.其次基于非负矩阵分解和K均值聚类实现了主特征分量分解和地震相聚类.实际地震数据应用结果和指标分析表明, 本文方法提取的隐含特征趋于正态分布, 且主特征分量中蕴含了不同地震相类别的响应, 从而可以获得更准确的地震相分类结果.在渤海湾盆地东营凹陷古近系沙河街组湖相沉积中, 清晰划分出了六类沉积微相的边界, 有利于揭示三角洲沉积环境演变.