Hyperspectral image is a new kind of remote sensing images with the feature of "combining mapping and spectra into one",thus better expressing the subtle differences on the surface of the material through the continuous spectral curve. Hyperspectral images have a wide range of applications in such aspects as classification,unmixing and target detection. With the continuous development of hyperspectral remote sensing technology,anomaly target detection has become one of the most active direction of research because it doesn't need a priori information. Many anomaly target detection algorithms have been proposed. Based on data available both in China and abroad,this paper summarized the research situation and new progress in anomaly detection algorithms. The author first expounded the essence of hyperspectral anomaly target detection and used the basic theory and then analyzed and summed up some representative anomaly detection algorithms in such aspects as the ideas of algorithm,key technology,advantages and disadvantages. On such a basis, the author summarized and described the evaluation method of anomaly detection and discussed the future development trend of anomaly target detection algorithm, with the purpose of finding new breakthroughs in the study of the algorithm of hyperspectral anomaly target detection.

In order to summarize the characteristics and key points of TLS and stress the importance of TLS technology in landslide monitoring,this paper briefly describes the principles of TLS technology and basic methods for data processing, gives a review of TLS application history and present research situation both in China and abroad, and makes classification for these researches and applications. In addition, the crucial aspects and the prospective development of TLS are discussed. Although TLS has not become one of conventional technologies in landslide monitoring yet, it has opened up a new field of research in landslide monitoring. And it continues to expand and develop.

Microbial reclamation is an effective means for ecological restoration in coal mining. Remote sensing, especially hyperspectral remote sensing, can rapidly and precisely determine the growth status of plants in the field and the improvement of soil properties, thus providing important technical support for implementation of microbial technology to land restoration and ecological environment rehabilitation in coal mining. This paper summarized in detail the application of remote sensing to monitoring the plants recovery, reclamation of waste dump of tail slag and subsidence and pollution of heavy metals in reclaimed areas, and the application of microbial technology to improving the environment of coal wastes, managing mining subsidence land and restoring land polluted by heavy metals. Some ideas of remote sensing technology applied in microbial reclamation for future research were discussed in this paper, which could provide an important reference and guideline for quick, accurate and undamaged monitoring of growth of plants reclaimed with microorganism and estimation of microbial technology implemented in ecological restoration in coal mining in the future.

Canopy data from hyperspectral remote sensing of irrigated land and dry land at different growth stages were used to analyze the difference between irrigated land and dry land. According to the bands of TM image,the measured spectrum was divided into four bands,the comparison of the spectra between irrigated land and dry land at each band was made and, on such a basis, the best band to identify irrigated land and dry land for spring wheat was chosen. The results show that canopy spectral reflectance in the visible region and in the near-infrared region of spring wheat has complete variation regularity: at the first band,the order is sunny land>double-sided land>shady land>irrigated land,which is opposite to the things of the near-infrared band. From setting to milk stage,in the visible band,spring wheat of irrigated land and dry land shows the order of setting stage>milk stage>jointing stage>heading stage>flowering stage; in the near-infrared band, the order is flowering stage>heading stage>jointing stage>setting stage>milk stage. The spectral curves are different because of chlorophyll content and coverage. In the visible band,spectral curves of spring wheat at setting and milk stage have their own lines but become two lines in the middle period; i.e.,sunny land and double-sided land are completely coincident with each other,whereas shady land and irrigated land are basically coincident with each other. The spectral range of 760~900 nm is the optimal band for identifying spring wheat in irrigated land and dry land.

The segmentation of LiDAR point cloud is a basic and key step in 3D reconstruction of architecture. Some problems such as under-segmentation or over-segmentation exist in current point cloud segmentation based on boundary, surface or clustering method. In this paper, a point data segmentation method based on similarity measures in multi-dimension Euclidean Space(SMMES)is presented. The main workflow of this method consists of calculating point normal vector,transforming the raw data combined with image features,calculating Euclidean distance in the multi-dimension space, comparing the similarity between the adjacent points,and segmenting the point data. The method proposed in this paper has solved the problem that geometry and spectral features cannot be used in parallel during the point cloud segmentation. In addition, it has the advantages of both geo-metrical segmentation and color-metrical segmentation, and can improve the accuracy of the point cloud segmentation. The segmentation results of the three different methods which are based on geometry features, spectral features and SMMES respectively were compared with each other by using two sets of data, and the experimental results show that the proposed method is significantly feasible and practical.

Small baseline subset(SBAS) algorithm has been widely applied to time series analysis for measuring surface deformation by overcoming InSAR limitations caused by temporal decorrelation, spatial decorrelation and atmospheric inhomogeneity. To monitor the ground deformation in Changzhi area effectively, the authors applied DInSAR method to study large deformation that happened in one month or even a shorter time in the mining area, and the maximum subsidence was 11cm during 30 days. Then the authors obtained the time series deformation results from July 2003 to July 2010 in Changzhi area by using SBAS algorithm, and made time series analysis of the residential area with large deformation rate which kept high coherence around the mining area by SBAS monitoring results. From the deformation maps, the authors found that subsidence was obvious in the study area, and the subsidence rate was 5~15mm/a, with the maximum accumulated subsidence being 90 mm. Different ore districts presented different deformation results due to their differences in such factors as production time, production way, reserves and topography.

The linear model is among the vital models in 3D visual scene, and its degree of fineness determines the visual effect of the scene. Common methods for constructing linear models can be divided into integral model method and spliced model method; nevertheless, for large-curvature linear models, neither method is ideal. The vertex coordinates and texture mapping are difficult to control when the integral model method is employed, and space between models and texture overlaps is inevitable when the other methods are used. A 3D visualization method for high-curvature linear entity is proposed in this paper. In the phase of geometric modeling, the vertex coordinates, normal vectors and index data are calculated for model lofting based on path and cross section. In the phase of texture mapping, textures are made by creating a mapping between vertices on the model and pixels on the photo. The results show that the algorithm can be utilized to make large-curvature linear models like railway roadbed models, and the algorithm has the merits of high accuracy, requirement of less manual work and good visual effect.

Due to the significant spectral difference in multimodal image registration, the rate of correctly matched feature-points calculated with point-feature detecting and matching algorithms, such as speed up Robust features,SURF, is quite low in some difficult cases. In order to solve this issue, this paper proposes a novel image registration approach with the help of point and curve features. It adopts the location information indexed by the correct matching pair of feature-points and transformation information determined by the pair of correct matching curves simultaneously. Also, the feasibility and advantages of this algorithm were all confirmed by the experiments in this paper. The results show that this method can achieve automatically the registration of the multi-modal remote sensing images in the completely unattended case and align some kinds of remote sensing images automatically. In addition, it is more robust and reliable.

Microwave land surface emissivity of the Taklimakan Desert was retrieved based on AMSR-E (advanced microwave scanning radiometer-earth observing system) Leval 2A measurements and land surface and atmosphere products from GDAS (global data assimilation system) in 2008 under clear atmospheric conditions. Then, the spectral characteristics of the retrieved emissivity were classified and analyzed with respect to the soil types, and the relationships between desert emissivity annual variability and climate factors were also analyzed. The analyses indicate that the desert microwave emissivity and its annual variability are closely related to soil types. Moreover, there is a significant correlation between soil volume water content as well as land skin temperature and inter-annual variations of emissivity, which decreases with both of the two land surface parameters. Furthermore, vegetation coverage, canopy water content and surface roughness depending on the soil moisture are also the influencing factors of emissivity, and the soil moisture is restricted by both the atmospheric total precipitable water and the underlying soil type. Additionally,the surface emissivity of the desert mostly composed of sand observably decreases with the desert depth.

Weighted filter empirical mode decomposition(WFEMD), as a new multi-scale and multi-resolution analysis algorithm, is more appropriate for the analysis of the image details than wavelet, super wavelet and dimensional empirical mode decomposition, and can solve the inherent defects of the traditional two-dimensional empirical mode decomposition(EMD). The main reason is that it directly computes the mean envelope by adaptive weighted mean filter. When WFEMD is introduced to the remote sensing image fusion, the characteristics of original images can be better extracted, and more information for fusion can be obtained. Firstly, the source images are decomposed by using WFEMD with the capability of acquirement of the high frequency data, the adaptability for some intrinsic mode functions (IMF) and the residual component, and then the IMFs and the residual component are fused with the details/background and average fusion regularity respectively at the corresponding scales. Finally, the fused IMFs and the residual component are reconstructed to obtain fusion results. Experiments have shown that the proposed algorithm is efficient in image fusion and is better than other current algorithms.

In view of the characteristics of SAR images stack data, this paper presents a method for extraction of the water area based on similarity analysis using pixel-level SAR image time series. The basic idea is as follows: Firstly, the pixel-level SAR image time series is constructed, and the dynamic time warping(DTW) is chosen as the similarity measurement to compute the similarities between all the pixels and sampled water pixel. Then, the water pixels are extracted with the threshold value method, referring to the DTW distance of mixed pixels on borders of water for threshold value. Lastly, by replacing the backward scattering coefficients with DTW distance of each pixel, the accuracy of water identification is improved by 8-connected method. 25 wide-swath ENVISAT ASAR images collected during the period from January to December in 2008, which have 150 m spatial resolution,were utilized to extract water pixels in the study area. The result shows that the water pixels extracted by this method have high accuracy and integrity,thus proving that this method is practical in the extraction and mapping of water body distribution at the large regional scale.

Validating the cloud detection result at night and distinguishing low cloud from fog through satellite data are difficult in southern mountain areas of China. In this paper, a method is presented by analyzing the new features of the visible infrared imaging radiometer suite (VIIRS) sensor data and the theory of the cloud detection. The viability of VIIRS day and night (DNB) data in night cloud detection is discussed in detail and the result shows that the DNB data can be used to validate the result when lunar zenith angle is less than 60°. The application and validation show that the method is effective, and the estimation accuracy is higher than 91% when scan angle is less than 15°, and the BT_M12-BT_M13 and BT_M12-BT_M15 can be used to effectively distinguish low clouds and fog. In addition, the detection thresholds are sensitive to the sensor zenith angle, and the detection accuracy is higher when the sensor zenith angle is small.

The successful launch of ZY-1 02C satellite has filled the gap in the high resolution remote sensing satellite developed by China itself. Based on the characteristics of the satellite image and data quality,the authors took ZY-1 02C P/MS sensor data of Russia's Siberia as an example to interpret the region's geological and mineral resources information for the purpose of analyzing the data processing and data quality of P/MS image in combination with the United States Landsat ETM⁺ data of the region for data fusion. Four kinds of fusion methods, i.e., HSV,Brovery,Gramm-Schmidt and PCA fusion, were used for experimental comparison. The experimental results show that the image fused with Gramm-Schmidt method not only suffers from the least loss of spectral information but also has the best fusion effect,thus improving the spatial resolution of the fused image effectively. In other words, the precision of remote sensing geological interpretation is greatly improved by Gramm-Schmidt method. The results obtained by the authors can play an active role in further promoting the application of the domestic high resolution remote sensing satellites in China.

In order to improve the collection efficiency of GCP and reduce the influence of human factors,the authors used the improved watershed segmentation and weighted neighbor detection methods to select the cross point of roads as GCP,and employed GrabCut segmentation and contour detection methods to select the center of gravity of the region as GCP in the reference image. The ALOS image with 2.5 m resolution was geometrically corrected by the GCPs which were selected from aerial images with 0.5 m resolution. The results show that the GCP selection methods proposed in this paper for remote sensing image geometric correction are less affected by human factors,the precision is less than 1 pixel,and hence the semi-automatic extraction can be realized. The technical achievement in this paper has been applied to the GCP selection for the production of DOM,with an important role played in the renewal of DOM at middle and small scales.

As the main element in the uranium ore deposit,the nuclides of ²³⁵U decay to release the factor heat,causing the formation of thermal infrared remote sensing anomalies around the uranium deposit genetically related to granite intrusive body. The Huashan granitic pluton in eastern Guangxi has good geological conditions for generating uranium ore deposits. In the inaccessible environment of harsh terrain,it is difficult to carry out field survey work with traditional methods. Using Landsat7 ETM⁺ 6 thermal infrared band data,the authors extracted the remote sensing thermal anomalies of the rock through digital image processing techniques,and established the ETM⁺ 6-γ spectrum anomalous inversion model to delineate the γ-spectrum abnormal field distribution of the whole granitic pluton. On such a basis,the authors screened the γ-spectroscopy anomalies according to the ore-controlling fracture structures and related geological and mineral data. At last, a number of uranium ore prospecting areas were delineated in Huashan granitic pluton. The results obtained by the authors not only provide an important clue for the further field prospecting work but also demonstrate the enormous potential of the geothermal anomalies detected through the inversion of thermal infrared remote sensing in the prediction of granite-type uranium ore deposits.

On April 20,2013,a catastrophic earthquake with M_S 7.0 occurred in Lushan County,Sichuan Province. Using the multi-source remote sensing data acquired before and after the earthquake,the authors analyzed the secondary geological disasters and their spatial distribution based on interactive visual interpretation and field survey. The remote sensing investigation results have shown that the earthquake has triggered 1 678 secondary geological disasters,covering an area of about 8.354 km². The secondary geological disasters are characterized by smaller scale and dominance of collapse and rockfall types. Using the terrain data before the earthquake,the authors analyzed the relationship between the distribution of secondary geological disasters and the elevation and slope. Statistical and analytical results show that 95% of the secondary geological disasters are located in the area with the elevation between 750~1 850 m,and 82.5% of the secondary geological disasters are located in the area with the slope between 15 °~ 50°. With the increasing slopes,however, the incidence of the secondary geological disasters increases significantly. The secondary geological disasters assume remarkable linear arrangements, with some distributed along the NE-trending seismogenic fault and the others along the mountain ridge and river valley. The results obtained by the authors provide some important information for the emergency decision-making,rescue and reconstruction after the earthquake.

Gas hydrate in permafrost gradually decomposes and seeps to atmosphere because of global warming,and the seepage changes the methane (CH₄) concentration of permafrost's atmosphere. In this paper, the possibility of gas hydrate detection with remote sensing data is proved by the nature gas detection experiments performed with domestic nature gas and ASD portable spectrometer. The experimental results show that two feature absorptions,spectra around 1 700 nm and 2 300 nm,are founded in the electromagnetic wave range from 380 to 2500 nm. Supported by the experiment,the authors selected the remote sensing data retrieved from the scanning imaging absorption spectrometer for atmospheric chartography (SCIAMACHY) sensor during the period from 2003 to 2006 to analyze temporal and spatial changes of atmospheric methane of the permafrost in the Qilian Mountain. In addition to the discovery of methane concentration's seasonal changes,the trend of its gradual increment characteristics was also detected. This phenomenon is considered to be the result of methane seepage from underground gas hydrate. Finally,in combination with the geological data, the atmospheric methane anomaly in February 2006 was used to evaluate the potential of gas hydrate of the permafrost in the Qilian Mountain and, as a result, a new prospecting area was recommended.

The salinized soil in northern Shaanxi Province was chosen as the study object. The hyperspectral data were collected and the soil samples were analyzed. First, the correlation between the soil salinity and the reflectance were analyzed, and the characteristic bands were fitted. The usual regression and partial least squares regression (PLSR) analysis was used to study the inversion model of soil salinity, and some testing samples were used to compare the accuracies. The results show that 482 nm, 1 365 nm, 1 384 nm, 2 202 nm and 2 353 nm are five characteristic wavelengths, and the precision of inversion is satisfactory. The result of precision test indicates that the inversion model with PLSR calculated by Matlab is fairly good, and the correlation between the measured value and the predicted value is better.

Tidal channels, vegetation types and FVC (fractional vegetation cover) were extracted from high resolution aerial images according to the field measurement. Combined with multi-temporal satellite images and numerical ocean model, the authors employed the waterline and the width-to-depth ratio methods to inverse the tide creek bottom elevation, with which the Dongtan 3D terrain of Chongming was constructed. The effects of vegetation on tidal creeks were analyzed and some conclusions were reached: 1 The root mean square error between the tidal channel calculation and the measured result is 0.545 m at Dongtan of Chongming, and the accuracy is higher at high tide beach than at the low tidal beach; 2 From low to high tide flat, tidal channel depth increases first and then decreases along the longitudinal profile. The shallow tidal creek at the high tidal flat results from the weakness of water dynamics and beach consolidation by the vegetation roots, where downward erosion is inhibited. On the other hand, tidal channel becomes wide and shallow at the low tidal flat owing to lateral erosion without vegetation cover and weak hydrodynamics; 3 Tidal channel depth is deep in the south and shallow in the south. The Dongtan tidal channel density and vegetation coverage show significant negative correlation (r=0.560 4, p<0.02), and tidal channel length has a significant corresponding relation with vegetation types. Tidal channel length is longer in Spartina alterniflora and reed communities than in Scirpus mariqueter areas. Tidal creeks are undeveloped in the tidal flat with high density vegetation coverage.

Sea ice classification is an important basis for the sea ice forecast in that many parameters such as the maximum of sea ice extent can be derived from that. An approach to sea ice classification using HJ-1C SAR data, which is the first civil spaceborne SAR system of China, is presented in this paper. The data were S-band and VV polarized and acquired over Liaodong Bay. Three types of information were extracted from the SAR data, including the uncalibrated backscattering coefficients and the gray level co-occurrence matrices. Besides, the level ice concentration was introduced as the classification basis. It proves to be effective in sea ice classification, especially in the separation between brash ice and wind-roughened open water. Based on all the information, the authors implemented the classifier fusion which combines the maximum likelihood and the decision tree. With the optical data from HJ-1B for validation, a good result is obtained, in which the brash ice, level ice and open water are well distinguished.

Arctic sea ice has become a hot topic in the research on globe climate change, because it has been affected by the global climate change and can in turn affect the global climate. The traditional survey methods are seriously limited by the severe climate and environment of Arctic area. The development of remote sensing, especially for the Synthetic Aperture Radar (SAR) and Polarimetric SAR, can yield more effective methods for data acquisition in the study of Arctic sea ice. In this paper, the TerraSAR-X polarimetric data and the SEATH(SEparability and THresholds)object-oriented algorithm have been introduced to evaluate the capability of polarimetric features in the Arctic sea ice classification, and some classification examples are presented to show their performances. The classification results demonstrate that the polarimetric features of |VV|, T11 and SPAN show a better performance in Arctic sea ice extraction. The achievement will provide a theoretical foundation for the classification of large-area Arctic sea ice and the parameter design of sea ice monitoring satellites.

The South China Sea has crucial geographical value as well as rich natural resources. We should strengthen the management of islands and reefs in this region in order to maintain China's ocean sovereignty and national interests. However, the South China Sea is an area where tropical storms take place frequently and the disasters resulting from these tropical storms seriously endanger people's lives and islands management. The research was based on tropical storm data from 1977 to 2012. The authors analyzed conditions and regularity of tropical storms which affected the South China Sea, including its islands and reefs, in the past 35 years through the methods of buffer analysis and statistics of spatially topological relations, with the purpose of providing some foundations of avoiding disasters resulting from tropical storms during the implementation of the management of the islands and reefs in the South China Sea. Some conclusions have been reached: 1 The region which is most frequently affected by tropical storms in the South China Sea is the ocean area (18°N~21°N,116°E~122°E) located from the Dongsha Islands to the Zhongsha Islands; 2 Tropical storms which impact islands and reefs in the South China Sea mainly take place in summer and autumn (June to November); 3 Among islands and reefs in the South China Sea, the Dongsha Islands seem to be an area most severely affected by tropical storms, followed by the Zhongsha Islands and the Xisha Islands, with the Spratlys being less affected by tropical storms.

With China's coastal zone as the research area, 6 ZY-3 sensor corrected products were chosen for evaluation, which covered Qingdao of Shandong, Yancheng of Jiangsu and Wenzhou of Zhejiang from north to south. The authors used high accuracy GPS points to evaluate the autonomous positioning precision in comparison with SPOT5 images. Then DOMs were generated with high accuracy GPS points as ground control points(GCPs)and national 1：50 000 DEM products, and the influences of the quantity and distribution of GCPs were analyzed.The results show that the average autonomous positioning precision of ZY-3 panchromatic image and that of multispectral image are 19.6 m and 23.0 m, respectively. The autonomous positioning precisions of ZY-3 panchromatic image and multispectral image are better than the precision of SPOT5 images. With 1 GCP at the four corners, the positioning precision of DOM is less than 2 pixels. With 4 GCPs, the positioning precision of DOM is about 1 pixel. With 2 landside corner GCPs, the positioning precision of DOM is less than 3 m. With 3 landside GCPs, the positioning precision of DOM is lower than that of 2 landside GCPs, especially at the seaside. The results show that, with a few GCPs, ZY-3 DOM can reach high accuracy and satisfy the need of coastal zone remote sensing survey.

Land surface temperature (LST) is an important parameter in such fields as meteorology, hydrology and ecology, and the inversion of land surface temperature by use of remote sensing data is a simple and effective method. In this paper, with the support of the 3S technology, the authors took Beijing City as the study area to study the land surface temperature inversion and analyze spatial and temporal characteristics. Using TM data obtained from 1989 to 2010 within the 5th Ring Rroad of Beijing, the authors adapted the single-window algorithm in 20a to the inversion of land surface temperature, with a comparative study of the verification of synchronous MODIS temperature products. On such a basis, the anomaly analysis and hatches analysis were conducted to reveal the overall characteristics of temporal and spatial variation of Beijing surface temperature in more than 20 a. The characteristics of temporal and spatial variation of NDMI and NDBI index were analyzed based on 20 years' land surface temperature of the study area, and the relationship between land surface temperature and NDMI as well as between land surface temperature and NDBI was quantitatively revealed and expounded. Research results show that the single-window algorithm method can obtain the reasonable land surface temperature and can reflect the spatial difference of surface heat condition. Beijing land surface temperature variation characteristics are obvious, surface temperature difference of different land cover types in the same period is evident, and heat island effect is also significant. A significant positive correlation between surface temperature and NDBI exists, and there is a significant negative correlation with NDMI, which means that both NDMI and NDBI are good indicators for marking surface thermal characteristics, and they can be used to provide an effective basis for monitoring surface temperature changes and its spatial and temporal characteristics.

It is important to expand the application of domestic satellite data such as China-Brazil Earth Resource Satellite(CBERS) images for backtracking interpretation research in such areas year-by-year. In this paper,with the Nanping City in northwestern Fujian Province as the study area,the authors collected the related eleven CBERS images acquired during 1999-2008,and established the image interpretation keys to the disasters. The visual interpretation method based on GIS platform was used in combination with the field studies,and a total of 2 059 interpreted disaster points were obtained during the past 8 years, with no data obtained in 2001 and 2002. The results of dynamic analysis show that the geological disasters in he study area were mainly the shallow landslides that concentratedly happened in 2000 and 2005. They reached 582 and 766 sites respectively, possessed 65% of the total landslides, and were distributed near residential and traffic districts, showing heterogeneous temporal and spatial characteristics. It is held that CBERS images can be employed for dynamic survey of geological disasters.

Qingchuan County of Guangyuan City in Sichuan Province is a national ecological pilot county and implementation country of returning farmland to forest. The understanding of the current situation and spatial-temporal dynamic change of land use/cover is of great significance in providing the scientific basis for relevant government departments. In this paper, with the application of RS and GIS technology, on the basis of TM images in 2000, 2005 and 2010, and through image processing and information extracting, the authors acquired land use/cover maps in different years and established the database. On such a basis, the land use/cover dynamic change process in Qingchuan in the past 10 years was analyzed, and the driving force that caused the change was identified. At last, land use/cover area ratio of the study area in 2015 and 2020 were predicted. According to the results obtained, between 2000 and 2005, the area of arable land, water and unused land decreased, while the area of woodland, grassland and construction land increased; between 2015 and 2020, the area of arable land and grassland decreased, while the area of woodland, water, construction land and unused land increased. During the ten years, the area of arable land continually decreased with the scale reduced, the area of woodland continually increased with the scale reduced, the area of construction land increased continually with the scale increased, the area of water and unused land decreased and then increased, and the area of grassland increased and then decreased. An analyses reveals that policy, economic development, population growth, and natural disasters seem to be the principal impact factors of land use changes in the study area. It is inferred that, under the conditions that relevant policy is unchanged and no natural disasters occur, the proportion of woodland area will increase from 58.57% in 2010 to 59.01% in 2015, and increase to 59.44% in 2020. The proportion of arable area will decrease to 29.13% and the proportion of construction land will continually increase to 0.22% by 2020.

At present, most of the remote sensing images are generated directly by remote sensing image processing software or produced by remote sensing image processing software combined with foreign GIS software,such as ArcGIS and MapInfo, but are rarely combined with domestic GIS software,such as MapGIS. Relying on the project of mineral inspection of law enforcement by satellite image, this study focused on solving the problems of automatic mapping and batch input of the mining data by practice. The authors also summarized the effective steps and methods of the application which combine remote sensing images with MapGIS. The results show that raster data of remote sensing images and vector data of MapGIS can be combined together conveniently and effectively, and it can produce the remote sensing images which meet the need of mineral inspection of law enforcement by Shandong satellite image.

The reconstruction of urban old districts has been implemented in many cities. In order to retain the three dimensional original status of the old district and develop the housebreaking policy for decision makers, the authors studied the method of three-dimensional modeling for demolition and relocation base with the aerial stereo pair image as the data source. With the known intrinsic and extrinsic parameters of the aerial stereo pair image, the DOM of the corresponding district was made by the LPS software. On the basis of the DOM, the DSM of the district was established by the Stereo Analyst model of ERDAS software. Meanwhile, texture extraction and mapping was done. At last, three dimensional visualization and analysis were made with the 3D Matrix software. The experimental results show that the proposed modeling method has a high speed and accuracy, and can quickly and accurately achieve the three-dimensional visualization of demolition and relocation base.

According to the process of major natural disaster emergency monitoring and evaluation and the requirement of the function of application demonstration, in combination with the integrated development technology of ComGIS,and on the basis of the development environment of JAVA and Microsoft.NET, the open source framework of GDAL and the map service of Geobeans, the authors integrated the Arc Engine platform and the Postgres database, and designed and developed the application demonstration system of major natural disaster emergency monitoring and evaluation. The authors also completed the database system, data processing and share service platform, computer business support platform and 6 demonstration systems, i.e., the service capabilities demonstration system of remote sensing disaster reduction, the application demonstration system of remote sensing disaster monitoring, the application demonstration system of risk assessment and early warning, the application demonstration system of remote sensing disaster assessment, the application demonstration system of disaster emergency support and the application demonstration system of the world integration information collection. And the system has been deployed from the central organ to the demonstration provinces. The system has realized the maximized sharing of the information and software and hardware resources, effectively reduced the threshold of the satellite data application and, with the domestic satellite data for the environment and disaster reduction satellite as the representative, improved the application level and utilization rate in disaster reduction, transportation and other industries.

In order to adapt the remote sensing technology to different users' needs and remote sensing technical development,the authors proposed a scalable software framework design method for remote sensing image processing. A software platform was developed,the unified remote sensing image format and access methods were formulated,the application program interfaces(API)of basic functions and expansion modules were provided,and the menus and corresponding responses were dynamically achieved when the expansion modules such as the executable program(EXE)and dynamic link library(DLL) were added to the platform. The data could be exchanged between the platform and expansion modules or between the extension modules. Researchers can develop new expansion modules which can be easily added to the platform based on the available functions in the platform without duplication of effort.