KEYWORDS: Data modeling, Remote sensing, Databases, Data storage, Image storage, Image compression, Systems modeling, Lithium, Data centers, Image retrieval
Owing to the rapid development of earth observation technology, the volume of spatial information is growing rapidly; therefore, improving query retrieval speed from large, rich data sources for remote-sensing data management systems is quite urgent. A global subdivision model, geographic coordinate subdivision grid with one-dimension integer coding on 2n-tree, which we propose as a solution, has been used in data management organizations. However, because a spatial object may cover several grids, ample data redundancy will occur when data are stored in relational databases. To solve this redundancy problem, we first combined the subdivision model with the spatial array database containing the inverted index. We proposed an improved approach for integrating and managing massive remote-sensing data. By adding a spatial code column in an array format in a database, spatial information in remote-sensing metadata can be stored and logically subdivided. We implemented our method in a Kingbase Enterprise Server database system and compared the results with the Oracle platform by simulating worldwide image data. Experimental results showed that our approach performed better than Oracle in terms of data integration and time and space efficiency. Our approach also offers an efficient storage management system for existing storage centers and management systems.
Hyperspectral Imaging Systems (HIS) are widely applied in many fields. However, in the traditional design of HIS, it is much time-consuming to acquire an integrated hyperspectral image. Compressive sensing is an efficient method to process sparse data, and a single-pixel camera which used the digital micromirror device (DMD) for accomplishing the CS algorithms had been developed. Nowadays, DMD achieved great development. The size of mirror array is increasing while switch speed of a single mirror becomes very fast. Consequently, researchers make efforts to design a HIS using CS method. CS method is a method to scale down the spatial information but the hyperspectral datacubes are still huge because of the thousands of bands. In this paper, we design a DMD-based spectrometer architecture using the method of compressed sensing principle, combined with DMD's spectral filter characteristics. In the new architecture, there are two DMDs. One is used for implementing the CS pattern, the other for filtering the bands. It has spectral simply adjustable advantages. With this new technology, we can reduce the amount of information which needs to be transmitted and processed in both spatial and spectral domain. We also present some simulation results of implementation procedures.
KEYWORDS: Geographic information systems, Data storage, Data modeling, Data backup, Virtual reality, Data conversion, Data storage servers, Computing systems, Computer programming, Data processing
Global GIS (G2IS) is a system, which supports the huge data process and the global direct manipulation on global grid
based on spheroid or ellipsoid surface. Based on global subdivision grid (GSG), Global GIS architecture is presented in
this paper, taking advantage of computer cluster theory, the space-time integration technology and the virtual reality
technology. Global GIS system architecture is composed of five layers, including data storage layer, data representation
layer, network and cluster layer, data management layer and data application layer. Thereinto, it is designed that
functions of four-level protocol framework and three-layer data management pattern of Global GIS based on
organization, management and publication of spatial information in this architecture. Three kinds of core supportive
technologies, which are computer cluster theory, the space-time integration technology and the virtual reality technology,
and its application pattern in the Global GIS are introduced in detail. The primary ideas of Global GIS in this paper will
be an important development tendency of GIS.
As one of the significant ecological environment problems, heavy metal pollution associates closely with environment
quality, human existence and security of food supplies. The remote sensing pollution mechanism in soil pollution-Cd is
discussed by researching into the status of rice leaf polluted-Cd in this paper. The response relationships between
remote sensing information parameters, which reflected the vegetation structure, physicochemical properties and
biologic parameters of soil-vegetation system, and soil polluted degree by Cd element are analyzed based on Hyperion
satellite data and a great number of ground experiment data. To extract remote sensing parameter to Cd pollution,
multiple discriminant analysis (MDA) was applied over the data, which is sensitive to rice chlorophyll, rice leaf
moisture, rice cell structure and rice LAI. The remote sensing mechanism models of Cd pollution in rice soil are
established, including MCARI-NDWI model, MCARI-RVSI model, MCARI-RVI model, NDWI-RVSI model, NDWIRVI
model and RVSI-RVI model. The research results indicated that the pollution monitoring of soil Cd element in
large scale might carry on initially according to these models, because different Cd pollution degrees are in different
positions of these models, however, the precision of pollution models need be further improved.
KEYWORDS: Geographic information systems, Data modeling, Spherical lenses, Computer programming, Data storage, Data backup, Document management, Databases, 3D modeling, Data processing
Global GIS is a system, which supports the huge data process and the global direct manipulation on global grid based on
spheroid or ellipsoid surface. A new Global GIS architecture based on STQIE model is designed in this paper, according
to the computer cluster theory, the space-time integration technology and the virtual real technology. There is four-level
protocol framework and three-layer data management pattern of Global GIS based on organization, management and
publication of spatial information in this architecture. In this paper a global 3D prototype system is developed taking
advantage of C++ language according to the above thought. This system integrated the simulation system with GIS, and
supported display of multi-resolution DEM, image and multi-dimensional static or dynamic 3D objects.
In order to efficiently store, utilize, manage and analyze the huge global spatial data and location based service,
alternatives to a global data model are urgently needed. A new global data structure, Multi-type Node Data Structure
(MTNDS) based on STQIE is proposed in this paper, to efficiently manipulate multi-resolution data and update data
dynamically, especially for vector data. There are two different types of 'node's in MTNDS, one is 'R_Node' (i.e. real-node),
the other is 'V_Node' (i.e. virtual-node). The point objects, the arc-line objects and the area (Curve surface)
objects are designed in detailed with two node types on STQIE ellipsoid surface. Then hierarchical MTNDS is organized
and designed, taking advantage of three object types. Taking example for area objects, the representation in different
levels and in different situations, including initial level and middle level, is proposed in this paper. Finally, the dynamic
manipulation of spatial data is discussed, taking advantage of the object search manipulation and the cartography
generalization.
Retrieval of concentrations of total suspended matter, chlorophyll, and CDOM constitutes a concern in remote sensing of Case 2 ocean water. Based on former research achievements, this study established a linear model of Reflectance of Remote Sensing (Rrs) of Case 2 coastal water of China. Experimental simulation was also carried out. The results show that the concentrations of TSM and chlorophyll are relatively less sensitive to the model errors, while CDOM is more sensitive. Remote sensing data of Yellow Sea and East Sea of China provided by National Satellite Ocean Application Service of China were applied in this paper. Spectral profiles calculated from the linear model show the similar trend with measured results form the Yellow Sea and the East Sea. The linear relationships between band 443 nm and band 490, and 510 and 555 nm match well with real situations. The spectral curves of TSM and chlorophyll in our model yield some useful information for calculating the concentration of CDOM.
Retrieving water components in case 2 waters by remote sensing is a crucial problem in evaluating ocean first productivity and monitoring various disasters. But it is difficult to accurately and universally develop both bio-optical models and remote-sensing reflectance model because independent temporal and spatial variation of dissolved organic matter (CDOM), chlorophyll and total suspended matter (TSM), high concentration of TSM, as well as the local characters of different regions. Currently Linear algorithms such as principal component analysis (PCA), factor analysis (FA), matrix inversion technique and semi-analytical algorithm are widely used in the field of ocean color. Remote sensing reflectance model is derived from the radiative transfer equation, which is significantly featured by non-linearity and negative feedback. In our study, the chlorophyll absorption model and some other parameters of bio-optical models are adjusted. The adjustment is based on the water components concentration measured simultaneously with remote sensing data in the Yellow Sea and the East Sea of China. Then the equation of remote-sensing reflectance model can be changed into linear matrix of water components and coefficients, we find the spectrum curves of total suspended matter coefficient and chlorophyll coefficient turn out significant negative correlation. As a result, when performing matrix retrieval algorithm, chlorophyll concentration and CDOM concentration are out of required accuracy except some special conditions. Experiment results suggested that the TSM had the greatest influence on the linear model.
In this study, Bayesian networks are considered to be a classifier for the remote sensing image named Aster data, which involves 15 bands. Six bands, which have different spatial resolutions, are selected to be the attributes in Bayesian network classifier. The sample data from Aster image that is fused by wavelet transform is used to train Bayesian network classifier. Before the above-mentioned processing, the attributes from the transformed image should be normalized by some equal width schemes. Then the learning scheme process is used to acquire the structure of Bayesian networks from the training data set. The relationship of the attributes among all the constituents of the imagery data is mined through the Bayesian networks. To evaluate this classifier, a comprehensive study of the performance is investigated based on the training data set and the independent test data sets. The result shows that Bayesian network performs well on remote sensing imagery data.
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