In order to enhance the automation and accuracy of road information extraction from the high-resolution remote sensing image (HRI), this paper proposes a HRI road segmentation algorithm, which includes 3 stages, i.e., spectral mergence, edge mergence, and road region extraction based on shape property. The first two stages are actually the image segmentation method based on region growing. Spectral statistic variables, such as average and variance, are considered in the spectral merging criteria to raise segmentation accuracy. A vector gradient method is used to accurately derive edge strength that is critical for edge merging criterion. Spectral and edge mergences are all implemented as global best merge algorithm, so the segmentation result is optimized. On the premise of the complete segmentation of the roads, shape properties can be effectively used to extract roads from HRI. Circularity is adopted to separate roads from non-road regions. Two scenes of OrbView3 multispectral images are used to carry out road extraction experiment. The experimental result shows that the overall accuracy and Kappa coefficient of the method proposed in this paper are above 97% and 0.8, respectively, obviously superior to the result of SVM supervised classification.

Accurate maps of the spatial and temporal dynamics of cyanobacteria blooms are urgently needed in the Taihu Lake, which is a drinking water resource for cities around the lake. Satellite imagery can be used as a cost-effective method for remotely monitor trends in cyanobacteria blooms. However, atmospheric effects and sun-target-satellite geometry can make multi-temporal images of blooms inconsistent with each other and cause uncertainties in bloom data extraction. In this paper, four remote sensing approaches were applied to retrieve cyanobacteria bloom information in the Taihu Lake during the whole year of 2006. These approaches included the near infrared (NIR) single band data, the ratio vegetation index (RVI), the normalized difference vegetation index (NDVI), and the normalized difference water index(NDWI). Two kinds of MODIS (moderate-resolution imaging spectroradiometer) products, i.e., the top-of-atmosphere (TOA) radiance images without atmospheric correction (MOD02) and the surface reflectance images with atmospheric correction (MOD09), were selected as the data source. Furthermore, three factors comprising the aerosol optical thickness (AOT), the solar zenith angle, and the sensor zenith angle were chosen as indicators of radiation transfer processes to evaluate their influence on the remote sensing indices during the extraction of cyanobacteria bloom information. Specifically, the relationships between retrieval threshold values and the three indicators were analyzed to evaluate the temporal influences quantitatively. The results showed that: ① these three factors had more impact on NIR single band data and the NDWI, and less impact on the RVI and NDVI (RVI was less sensitive than NDVI in regard to the atmospheric factors); ② both AOT and the solar zenith angle were positively correlated with the threshold values. Whether or not these relationships hold water for other cases needs to be further examined. It is thus held that these four remote sensing approaches should be used carefully for monitoring cyanobacteria blooms when atmospheric correction is not applied.

In order to meet high requirement of orthophotoimages in special Karst landform, the authors chose Guohua rocky desertification monitoring area as the study area. DEM was built by OrthoEngine module of PCI based on 1:10 000 digitization contour map, and orthorectification of ALOS panchromatic image was rectified by RPC function model. Then, pan and multispectral data were merged with high fidelitily by PANSHARP fusion algorithm. Fusion orthophotoimages with spatial resolution of 2.5 m were completed after modification. This means can provide a referencre for high precision images and abundant information data for scientific research in Karst areas. In addition, it also provides a new scientific information updating way for Karst rock desertification monitoring.

The extraction of the information of building height from the high-resolution remote sensing image is a hot research topic in the study of the information extraction. This paper proposes a set of building height inversion techniques in combination with the object-oriented classification method. First of all, through the object-oriented classification method, the building outline and shadow information are extracted by using morphological building and shadow indices; and then, the intersection line average method is employed to calculate the length of the shadow; finally, the height of building is calculated according to the geometric relation model between the shadow length and the building height. The proposed method was validated on domestic ZY-3 satellite remote sensing data of Xi'an City, and the overall accuracy of 91.23% was obtained by verifying the result using the actual measuring height of 171 buildings. The results achieved by the authors have a certain practical significance for the study of building height information extraction.

As a new style of remote sensing data, Pleiades satellite images haven't been used for a long time. There is a lack of ortho-rectification processing module dedicated to Pleiades satellite images. Taking 6 scenes of Pleiades satellite images of Daye-Yangxin area in Hubei Province as an example and using C# language, the authors developed a conversion program package of RPC files for Pleiades satellite images in the Visual Studio 2008, which is used to transform RPC files of Pleiades satellite images into RPC files of IKONOS satellite images. Therefore, the ortho-rectification model for the IKONOS satellite image in ERDAS9.2 can be used to ortho-rectify the Pleiades satellite image. The related problems concerning ortho-rectification processing of the Pleiades satellite images were solved by this method. The conversion program package of RPC files can be used to batch format conversion of Pleiades satellite RPC files and improve the conversion efficiency and accuracy. This method will provide support for the large-scale application of Pleiades satellite images.

Taking the SPOT6 satellite data acquired in September 2012 as the information sources and based on three aspects of key techniques comprising ortho-rectification, image fusion and band combination, the authors carried out a preliminary study of the satellite image processing. The results show that, in the ortho-rectification processing under the premise of the same auxiliary information with high accuracy, the data corrected by rational function model (RFM)and rigorous physical model(RPM)can meet the mapping accuracy of the ortho image at 1:15 000 scale, whereas the precision of the RPM corrected image is better than that of the RFM. In addition, in the GCP number demand, the RPM needs at least 8 points, whereas the RFM needs only 3~4 points. In image fusion, PanSharp transform fusion image has comprehensive advantages in information content, spectral feature and edge feature. In the band combination selection, it is considered that the band combination of B4(R)B1(G)[(B2+B3)/2](B)is the best for the purpose of extracting information. As for the purpose of drawing, the selection of the band combination of B3(R)B2(G)B1(B) can obtain better effect.

Scale invariant feature transform(SIFT) is a popular feature extraction algorithm that has applied to remote sensing image automatic registration; nevertheless, there exists a problem in the remote sensing image automatic registration based on SIFT algorithm, i.e., the distribution of feature points is always nonuniform. An automatic image registration algorithm based on potential function model is presented in this paper, which can solve the problem of optimizing nonuniformity in feature point distribution in SIFT. By adjusting the threshold of SIFT, the number of matching points is promoted. The algorithm can optimize the uniformity in feature point distribution by potential model function in molecular mechanics, and make the low-precision feature point to the sparse area of feature points. Then it revises local mutual information to improve matching point accuracy, so as to realize a high quality (uniform space distribution, high accuracy of Sub-Pixel registration) automatic image registration finally.

To fully utilize the spectral and spatial information rich in hyperspectral remote sensing images, this paper proposes a hyperspectral images classification method based on multiple kernel support vector machine (MKSVM) and Markov random field (MRF). Firstly, the MKSVM classifier is used to classify hyperspectral images, then the MRF is used to regularize the initial classification results in the spatial structure, and the final classification results are obtained in the end. The experiment on AVIRIS hyperspectral image shows that the proposed method not only effectively eliminates the "noise" in the homogeneous regions within the classification results but also improves the classification accuracy by about 3%.

Operational bloom remote sensing monitoring usually uses MODIS data with 250 meter resolution. However, most of the remote sensing image pixels are mixture of water and algae bloom. Using images with 250 meters resolution to extract algae bloom area will seriously affect the accuracy of algal bloom monitoring. Aimed at solving this problem and based on the mixed pixel model, the authors used the decomposition of mixed pixels to extract bloom component abundance in the mixed pixels. Compared with the traditional methods, the approach proposed in this paper improves the extraction accuracy of algae bloom area by nearly 30 percent; in addition, this approach is capable of reaching the algae bloom area extraction at the sub-pixel level, thus improving the accuracy of remote sensing. In practical application,this approach can extract algae bloom area by using DN values of remote sensing image without the pre-processing of radiation and atmospheric correction for remote sensing image.

Since the building profile obtained by segmentation or other methods has the disadvantages of inaccuracy or irregularity, this paper presents a new approach to extract the outlines of buildings: Firstly, images are preprocessed by combining spectral characteristics and multi-angle texture characteristics obtained by one dimensional Gabor filter of images to form characteristics to be segmented. On the basis of the construction of graph by Gaussian mixture model, the candidate points of the building can be determined by graph cuts, and the building blob can be obtained by mathematical morphology. Then according to segmentation objects, the main direction of the building is detected by the Radon transform, the least square matching templates are created, and the corner points are extracted precisely in the outline buffer zone. Finally, the accurate corner points are connected to constitute the outlines of the building. This method was tested by using synthetic image and high resolution images. The experimental result proves that this method is feasible.

To tackle the problem of urban area detection using high-resolution remote sensing images, this paper proposes a method based on Gabor filtering and density of local feature points by analyzing the residential area texture of high resolution image. For obtaining the amplitude information in multiple directions, the Gabor filtering was used firstly, and then the image feature points were extracted by subsequent processing of amplitude images. By computing the density of local feature points, the initial residential areas could be obtained. With further mathematical morphology transformation of the areas, the results were optimized ultimately. In the experiments, two WorldView2 data were used to validate the different methods. A comparative analysis with other methods shows that the method proposed in this paper has higher extraction accuracy and computational efficiency for urban area detection.

MODIS vegetation index time series of products can reflect the coverage of vegetation continuously, and is an important data source for remote sensing of crops. The authors selected Hubei Province as the study area. Using the MODIS-normalized difference vegetation index(MODIS-NDVI) data of 75 phases in the period from 2008 to 2013, and combining the crop phenology with samples of ground survey and other auxiliary details, the authors established the model for extracting the area of rape growing, and then extracted the distribution of rape growing in Hubei Province in the period from 2009 to 2013. The overall accuracy of the extraction was 85%, as shown by the verification of the HJ-1A CCD data of small environmental satellite, and this indicates that the reliability of the data of MODIS vegetation index time series and the study methods proposed in this paper for the extraction of the area of rape seed cultivation. The results achieved by the authors are very important for detecting the area of rape growing and information of output, reinforcing the management of agricultural production, adjusting the structure of agriculture, and assisting the government to formulate the agricultural policy scientifically and rationally.

To explore the correlation between the ecological parameters of Karst peaks and the dissolution rate of surface limestone bedrock, the authors selected the relevant ecological parameters to indirectly estimate the dissolution rate of limestone bedrock under the soil surface and, based on TM multi-band data of Landsat5, chose Karst area of Yaji Village in Guilin as the study area for the purpose of extracting its sensing parameters comprising NDVI, ground temperature and soil moisture. Using SPSS statistical software, the authors made a correlation analysis of these three factors with the limestone dissolution rate and obtained their correlation coefficients, which are-0.91, 0.85 and 0.93 respectively. A computing remote inversion model of limestone surface dissolution amount dissolution rate, which is covered by vegetation, was established by using NDVI to perform estimatation through regression analysis. The results show that NDVI and limestone dissolution rate have the maximum correlation. Therefore, the vegetation information is an indirect sign of dissolution of limestone bedrock surface. There is a linear relationship between the dissolution rate and the NDVI index. So long as the NDVI index is known in other parts of the study area, the limestone dissolution rate in this area can be estimated.

In this paper, the authors calculated the amount of the actual evapotranspiration based respectively on double layer model and improved double layer model in consideration of the available water rate of soil with MODIS data and meteorological data so as to investigate the temporal and spatial distribution of soil moisture and dynamic changes in Xuzhou City, Jiangsu Province. The amount of the potential evapotranspiration was calculated by using Penman-Monteith formula. Models were built to estimate the relative content of water of Xuzhou in July and November 2010, by crop water stress index(CWSI) obtained by the actual evapotranspiration and the potential evapotranspiration. The result shows that the relative error of the estimated data based on the improved double layer model and that of the measured data are 3.47% and 6.03% respectively, with the correlation coefficient being 0.84 and 0.84, which are better than the results obtained by the model based on the double layer model, whose relative error is 5.89% and 9.6%, and whose correlation coefficient is 0.53 and 0.72.

To accurately map the spatial-temporal variability of snow depth in Northern Xinjiang, the authors analyzed the spatial autocorrelation of monthly mean snow depths of 48 meteorological stations from December 2006 to January 2007, and investigated the relationship between snow depth, longitude, latitude and elevation. A multivariate nonlinear regression Kriging (MNRK) model based on the MODIS snow cover data is proposed to predict the spatial patterns of monthly mean snow depth. Relative to the ordinary Kriging (OK) and CoKriging with elevation (CoK) as covariate, the relative root mean square error(RRMSE) of predicted snow depth decreased by 15.14% and 9.54% in December, and decreased by 4.8% and 6.7% in January. The comparative results show that the MNRK method outperforms the other two methods. Integrating more information related to snow depth, the MNRK method is more efficient in capturing more spatial details of snow depth which varies with longitude, latitude and elevation. The CoK method without significantly correlated covariate produces worse results than the OK method.

Based on 2010 Landsat TM/ETM⁺ satellite remote sensing imagery data and referring to Google Earth imageries and other historical documents, the authors used object-oriented classification method to extract inland water bodies across China. The authors set imagery segmentation scale at 30 m, and extracted water bodies with ratio index, Normalized Difference Water Index (NDWI) and spectral relation. There are three types of water, i.e., rivers, lakes, and reservoirs. Finally, the spatial distribution of lakes in China was obtained. Using ArcGIS software package, lake area, shoreline length, shape index (SI), related circumscribing circle (RCC), fractal dimension (FD) and other landscape indices, the authors conducted calculation, and divided China into five lake districts according to previous researches. The differences of lake morphology were comparatively analyzed in the five lake districts. The results show that the object-oriented classification method has better classification accuracy. The overall accuracy of the three types of water is 93%, and the classification accuracy of the lake is over 90%. Statistically, the number of lakes with area greater than 1.0 km² is 2 477, totaling 77 934.72 km². The Qinghai-Tibet Plateau Lake District (QTPLD) has the largest lake area, accounting for 54.34% of the total lake area in China. The averages of SI and FD are the minimum, thus the lake morphology is simple in the QTPLD. However, the rainfall is abundant and the water system is well developed in Eastern Plain Lake District (EPLD). Its averages of SI and FD are the maximum among all the sub-lake regions. The lake shoreline is more zigzag, and hence the lake morphology is more complex in the EPLD. In addition, SI and FD have significant correlation (R² = 0.95). The higher the FD of the lake, the greater the observed SI.

The Qinghai-Tibet Plateau is an area sensitive to global climate change, and its wetland status plays an important role in the ecological security of the Plateau. Based on the remote sensing data of MSS in 1975, TM in 1990, ETM in 2000 and CBERS in 2006, the author established the interpretation keys, and obtained the wetland information data of the four periods by visual and human-computer interactive interpretation,in combination with the field data accumulated in quite a few years. The annual average precipitation and temperature spatial data were obtained by Kriging spatial interpolation processing for each year from 1962 to 2007 in the Qinghai-Tibet Plateau. With the phase distribution of the remote sensing data as the control layer, the temperature mosaic images of four periods and the precipitation mosaic images were generated by Grid computation using ArcInfo software. Using the pixel-based correlation analysis, the partial correlation analysis and multiple correlation analysis, the author analyzed quantitatively the relationship of spatial response between wetland change and climatic factors (temperature and precipitation) with AML macro language. The results show that the total area of wetlands decreased continuously in the Qinghai-Tibet Plateau from 1975 to 2000, but the total area of wetlands increased after 2000. The responses of wetland change to precipitation in the Chaidam Basin, Hexi Corridor and the Yellow River Basin characterized by dry climate are sensitive. Following the overall warming in the Qinghai-Tibet Plateau, the responses of the changes of the wetland supplied by the glacial melt water to the temperature of the region are sensitive, especially in the case of the large magnitude of warming in the low-temperature region.

In order to explore the relationship between climate variation and vegetation change in Anning River Basin in the period from 2001 to 2008, the authors chose MODIS NDVI, precipitation data and temperature data as study materials and conducted regression analysis and other methods to obtain spatial-temporal change patterns of precipitation, temperature and NDVI in Anning River Basin; meanwhile, the response mechanism of vegetation change to climatic variations in Anning River Basin was investigated. The results show that NDVI is positively correlated with temperature and precipitation, and the precipitation and temperature affect the change of vegetation directly, but the influence of precipitation is more significant than that of temperature. In addition, there exists the feature of time-lag between vegetation change and climate variation.

Vegetation is one of the most important components of terrestrial ecosystem. The dynamics of vegetation is affected by climate factors and human activities. Generally, natural vegetation is mainly affected by climate factors, especially temperature and precipitation. Presently, global climate change has occurred. To predict the influence of global climate change on vegetation, reduce the unfavorable or adverse effect and protect the ecosystem environment, it is necessary to study the correlation between vegetation and climate factors. Normalized difference vegetation index (NDVI) is generally regarded as a good indicator of terrestrial vegetation. In this study, ten-year MODIS NDVI time-series data and temperature, precipitation data from 2000 to 2009 in Circum-Bohai Sea area were used. A modified SPLINE method was adopted to remove the effect of noise and to reconstruct NDVI time series. On the basis of these data, the temporal and spatial dynamics of NDVI were analyzed through least-square regression. The correlation and lagged correlation coefficients between NDVI and temperature, precipitation were calculated. The results showed that an increasing trend in NDVI was found in the study area, but there was an obvious heterogeneity. The areas with significant increase and decrease in NDVI were small. In the Circum-Bohai Sea area, the development of vegetation was driven by temperature and precipitation, and the correlation coefficients between vegetation NDVI and temperature were greater than those between vegetation NDVI and precipitation in the same period. The response of vegetation to temperature and precipitation lagged and the lag period was about 16 days. Inter-annual NDVI change in the Circum-Bohai Sea area was mainly influenced by precipitation and human activities, and a positive correlation between precipitation and annual NDVI was found. Human activities influenced vegetation dynamics through changing land use. Urban sprawl led to decrease in NDVI, but the effort to return farmland to forest increased NDVI.

The utilization of multi-spectral remote sensing data to extract anomalies in western China is a fast and economical method, but the spatial resolution of the data available is low and the ferric contamination anomaly appears as mixed pixels so that the accuracy of extraction for ferric contamination anomaly is poor. In view of the deficiency of extraction for ferric contamination anomaly with low resolution remote sensing data, the authors, through analyzing the geological basis and spectral features of ferric contamination anomaly, determined the wave bands of high resolution remote sensing data, such as WorldView2 and IKONOS, and used principal component analysis to extract ferric contamination anomaly. A large-sized iron ore belt in Heiqia of Western Kunlun Mountains was found, and the ferric contamination anomalies coincident with the magnetite mineralization in Laobing of Western Kunlun Mountains were extracted. The results show that the accuracy of extracting ferric contamination anomaly with high resolution remote sensing data is satisfactory.

Located in the East Kunlun orogenic belt of Qinghai Province, Asiha-Annage area is one of China's famous metallogenic belts. The geological structure in the area is complex, and the metal mineralization is also extremely rich. In addition, the metallogenic potential is tremendous. In order to establish a system of mineral combination evaluation method for gold exploration in the region, the authors established the metallogenic geological remote sensing information extraction scheme as the center of prospecting technology according to regional geological characteristics and metallogenic geological conditions in area from Asiha to Annage and in combination with the theory of deposit geological remote sensing. On such a basis, different resolution images such as QuickBird,ASTER and ETM⁺ were interpreted for the line-ring structure and tone anomaly in the whole study area. Furthermore, the authors analyzed the image characteristics of regional typical gold deposits, found the line-ring structure favorable for mineralization and delineated 20 prospective ore-search areas. A comparative analysis of the tone anomalies and geochemical anomalies shows that the degree of the coincidence between the two kinds of anomalies is high and significant to the high efficient ore prospecting.

In order to study basic geology and distruibution regularity of mineral resources in Europe, the authors used Landsat ETM⁺ data as the main information source in this study and, by means of remote sensing information technology, investigated characteristics of remote sensing images of Europe. On the basis of remote sensing geological interpretation of strata, rock mass and structure, combined with relevant data of geology and mineral resources, the authors employed the remote sensing interpretation map of geology and mineral resources of Europe at the scale of 1:5 000 000 and some other maps, and found some new geological bodies and different phenomena. Then the authors put forward some new opinions concerning basic geology and mineral distribution regularity in Europe. Finally, eight strategic areas which are beneficial to mineral exploration were delineated. All these achirvements provide a first-choice objective support for overseas investment and following geological mineral exploration in Europe.

In order to offer remote sensing information support to geological and mineral resources survey and exploration overseas, the authors conducted further study of geological phenomena and structural characteristics in target areas of Tajikistan on the basis of previous results and current field exploration, established interpretation marks for sedimentary rocks, metamorphic rocks, intrusive rocks, fault structure, fold structure and ring structure in southern Tajikistan area with reference to ring geomorphologic landscape, tune, and stripes shown on remote sensing images gathered by ZY-1 02C satellite. A comparative analysis of panchromatic multispectral camera(P/MS) data and panchromatic high-resolution camera(HR)data received by ZY-1 02C satellite demonstrates that the color of geological bodies in P/MS has stark contrast so that it is more convenient to establish regional tectonic structure and carry out interpretation on rock categories, whereas micro-topography, grains and details of geological bodies shown in HR are clearer, which is suitable for identifying lithological charateistics and minor structures, especially for types of stratified structure, granularity of clastic rocks, Quaternary, mining activities and rocks with unique components.

Usually remote sensing data are set corresponding parameters to solve specific problems such as resource and environment; nevertheless, the geological structures from the regional scale to the hand specimen are divided into different scales, and hence a single kind of remote sensing data cannot meet the multi-scale geological structure interpretation. To solve this problem, the authors took Qiangduo area in Tibet as the study area, utilized the advantages of ETM⁺,ASTER, WorldView2 and DEM data of the study area to interpret the geological structures at two different levels from ETM⁺ data with 30 m resolution and ASTER data with 15 m resolution to WorldView2 data with 0.5 m resolution, and obtained a good result. Firstly, the interpretation of structural framework from ETM⁺ data was carried out by interpretation keys. At the same time, the ASTER band calculation was used to indirectly reflect structural information which could verify the results of the ETM⁺. Furthermore, the high spatial resolution WorldView2 data were integrated to analyze the structure. Finally, on the basis of the field validation, the interpretation results were revised. The geological structure interpretation results of Qiangduo area show that the integrated application of multi-source remote sensing data can improve the accuracy of structure interpretation and achieve a good practical application effect in a short period of time.

The mining wasteland reclamation and utilization proposed in the past two years are of great importance for resource-exhausted cities. The comprehensive benefits evaluation of mining wasteland reclamation and utilization in resource-exhausted cities can objectively quantify the effects of land resources utilization. This study adopted the latest survey data in 2013, integrated the traditional classic methods such as Delphi and AHP with the new methods such as remote sensing and GIS, and took the Qiyueshan mining area of Huangshi City as the study area. According to the characteristics of mining wasteland reclamation, this paper deals with the research idea and evaluation method of the comprehensive benefits evaluation of the mining wasteland reclamation and utilization in the resource-exhausted city, which reflect the overall effect and individual indicator changes of mining wasteland before and after reclamation objectively and quantitatively. The results show the good overall effect of the study area's mining wasteland reclamation and utilization, except that the improvement of some monomial index scores is not obvious. Due attention should be paid to the coordinated development of the economic, social, ecological and landscape benefits in future practices.

More and more serious environment problems have been caused by mineral resources development and utilization. In order to provide the visual reference for the dynamic adjustment of "the second round planning" and the basic information for "mine greening" action, the authors chose the Yuanyang mining area as the study area, utilized RS and GIS techniques to acquire mining development information, combined the data of physical geography and basic geology, used the grid method to begin the mine geological environment assessment and, on such a basis, compiled mine environmental recovery and management planning in a exploratory way. The results show that the planning is better than "the second round planning".

As one of visualization parameters for two-dimensional relief, Openness does not require light source and is not affected by the shadow of adjacent terrain. The parameter places more emphasis on the concave and convex of the terrain, which improves the intuitive stereoscopic impression of images. Openness can be calculated from multiple DEM sources, and can analyze the landform characteristics at different scales as well. The estuary of the Minjiang Estuary has various types of landform structure; therefore, the authors chose the area as a study area and calculated its Openness parameter on the basis of SRTM (90 m) elevation data so as to study the initial application of the parameter in terms of topography. The results show that, in comparison with traditional two-dimensional visualization methods such as shaded relief, Openness could display a better image which highlights the concave and convex of the terrain and reflects more details of the topography.

Shadow detection and compensation involve such problems as the uncertainty of remote sensing images, the complexity of algorithm and the low degree of automatic extraction. In view of this situation, the authors have designed an integrated experimental system based on the algorithm built for high-resolution remote sensing image shadow detection and compensation on the ArcGIS Engine platform. The system also utilizes Matlab and GDAL. Some key technologies such as data block reading, 2% linear stretch and DLL are used in the shadow detection and compensation system implementation, which solves some problems such as reading large quantities of data, uncertainty of image, and extensibility of the system. The system achieves the integration and optimization of the system, and improves the operating efficiency. Experimental results show that the system performs higher precision and efficiency in shadow detection and compensation for high-resolution remote sensing images such as QuickBird and ZY-3. Therefore, the system can be used for batch processing of remote sensing image data.

With the rapid development of the Beidou positioning system, GIS, network communications and smart devices, the embedded mobile GIS based on smart phones would have some remarkable functions, such as a high-precision positioning, field data acquisition, and real-time dynamic data transmission. In this paper, ArcGIS for Android of embedded GIS products, wireless communication technology, GPS technology and mobile computing equipment are combined with each other, and an embedded spatial data acquisition system based on smart phones is developed. This system has many functions, such as GPS positioning, field data collection, coordinate conversion, data pre-processing, data storage and output, and can provide technical support for land, mapping, power and other sectors in terms of rapid access to spatial data.

With rapid economic development in tourism market, traditional residential settlement environment is suffering damages and pressures from various aspects, which are likely to exert considerably adverse effects in terms of inheriting and promoting the traditional culture and history. Therefore, how to manage and protect traditional residential settlement infrastructure and the environment scientifically has become one of the most urgent issues. In the light of the characteristics of the traditional residential settlement, this paper innovatively presents the multi-technology-based collaborative environment of traditional residential settlement data collection methods that integrate the technologies of GIS, 360° panoramic image, mobile mapping system(MMS), aerial photogrammetry, global navigation satellite system(GNSS), location based service(LBS) and internet of things as well as the multi-source spatial data storage solution based geodatabase. Finally, a fully functional software system is developed to achieve the purpose of traditional residential settlement intelligent management and tourist self-service. Practice shows that the system significantly improves the intelligent level of traditional residential settlement infrastructure management and tourism services.