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Lu X.,Peking University | Cheng C.,Peking University | Gong J.,Wuhan University | Guan L.,Beijing Institute of Surveying and Mapping
Science China Technological Sciences

Aiming at the storage and management problems of massive remote sensing data, this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abroad. They mainly include the NASA EOS, World Wind, Google Earth, Google Maps, Bing Maps, Microsoft TerraServer, ESA, Earth Simulator, GeoEye, Map World, China Centre for Resources Satellite Data and Application, National Satellite Meteorological Centre, and National Satellite Ocean Application Service. By summing up the practical data storage and management technologies in terms of remote sensing data storage organization and storage architecture, it will be helpful to seek more suitable techniques and methods for massive remote sensing data storage and management. © 2011 Science China Press and Springer-Verlag Berlin Heidelberg. Source

Toutin T.,Canada Center For Remote Sensing | Zhu X.,Beijing Institute of Surveying and Mapping
International Geoscience and Remote Sensing Symposium (IGARSS)

Due to the specificity of space borne synthetic aperture radar (SAR) sensors, the stereoscopic pair can only be generated in across-track direction. However, the stereo can also be generated with triplet images when SAR system can span large values of incidence angle, such as Radarsat, TerraSAR and CosmoSky-Med. In this paper, the triplet stereo was introduced to high-resolution Radarsat-2 data for generating DTM. For this study, three high-resolution images from Radarsat-2 were acquired with 31°, 38° and 47° incidence angles over a rolling topography of north of Quebec City, Canada. Two main steps of generation of DTM, including the geometry for 3-D modeling and the radiometry for image matching were carried out. For 3-D modeling, one deterministic model, Tontin's 3-D Radargrammetric Model, was applied to mono/stereo/triplet Radarsat-2 data. For image matching, a NCC algorithm was used. The differential GPS points and LiDAR products were used as reference data. Preliminaries results of triplet stereo with simulated SAR HR data of the new Canadian Radar Constellation Mission will be presented at IGARSSS 2012. © 2012 IEEE. Source

Li L.,Wuhan University | Li L.,CSIRO | Chen Y.,CSIRO | Yu X.,Beijing Institute of Surveying and Mapping | And 3 more authors.
ISPRS Journal of Photogrammetry and Remote Sensing

The study of flood inundation is significant to human life and social economy. Remote sensing technology has provided an effective way to study the spatial and temporal characteristics of inundation. Remotely sensed images with high temporal resolutions are widely used in mapping inundation. However, mixed pixels do exist due to their relatively low spatial resolutions. One of the most popular approaches to resolve this issue is sub-pixel mapping. In this paper, a novel discrete particle swarm optimization (DPSO) based sub-pixel flood inundation mapping (DPSO-SFIM) method is proposed to achieve an improved accuracy in mapping inundation at a sub-pixel scale. The evaluation criterion for sub-pixel inundation mapping is formulated. The DPSO-SFIM algorithm is developed, including particle discrete encoding, fitness function designing and swarm search strategy. The accuracy of DPSO-SFIM in mapping inundation at a sub-pixel scale was evaluated using Landsat ETM. +. images from study areas in Australia and China. The results show that DPSO-SFIM consistently outperformed the four traditional SFIM methods in these study areas. A sensitivity analysis of DPSO-SFIM was also carried out to evaluate its performances. It is hoped that the results of this study will enhance the application of medium-low spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Source

Zhang X.,Peking University | Zhao J.,Beijing Institute of Surveying and Mapping | Tian J.,Clark University
IEEE Transactions on Geoscience and Remote Sensing

Optical remote sensing has been widely used to estimate soil moisture. However, modeling soil moisture dynamics across a large area based on remotely sensed optical data still poses a problem because of its spatial discontinuity due to cloud contamination. This study proposes a multisensor strategy for better mapping surface soil moisture on a daily basis at a regional scale. The basic idea is to decompose the surface soil moisture at any location into two terms, namely, baseline value in an observed period and daily variation, and to estimate for each term differently. For a certain day of interest, the corresponding 16-day composite of Moderate Resolution Imaging Spectroradiometer (MODIS) data is used to estimate the soil moisture baseline values across space, and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data are employed to estimate the daily variations. The proposed model was applied to produce daily surface soil moisture maps at a 1-km resolution for the fairly large study area of Xinjiang, China, regardless of the local weather conditions. It was found that the integrated use of MODIS and AMSR-E data was able to achieve significantly higher accuracy in surface soil moisture estimation (with a root-mean-square error of 3.99% in May and 4.43% in August, 2009) than the approaches based on either data alone could. The proposed model is expected to perform well for mapping surface soil moisture in other arid areas after the required parameters are calibrated with the local field data. © 1980-2012 IEEE. Source

Yu X.,Beijing Institute of Surveying and Mapping | Zheng Z.,Wuhan University
Acta Geodaetica et Cartographica Sinica

In general, Bayesian networks represent the joint probability distribution and omain (or expert) knowledge in a compact way and provide a comprehensive method of representing relationships and influences among nodes (or feature variables) with a graphical diagram. Accordingly, by advantages of Bayesian networks a new road to texture classification of aerial images for achieving the automatization and intelligentization of photogram-metry and remote sensing can be explored. In this paper, a new method is proposed to extract semantic feature based on classifiers, which constructs the mapping from low-level features to high-level semantic feature. Then it is applied to classification of aerial images' building and shrub. The experiment results demonstrate that the new method can improve the classification precision. Source

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