The cloudy and raining atmosphere can be detected by the microwave atmospheric sounding system in polar orbit meteorology satellites, but its long sounding cycle is a great limit for small and medium scale of severe weather (SMSSW) monitoring. Although SMSSW can be detected by geostationary earth orbit satellites (GEOS) with high temporal resolution, internal atmospheric parameters cannot be acquired because of the lack of microwave sounder on GEOS. Working in concert, microwave atmospheric sounding and GEOS at high-temporal resolution together comprise the geostationary earth orbit microwave atmospheric sounding (GEOMAS) system. Developing GEOMAS technology is of great significance for improving SMSSW forecast in all-weather and all-time sounding. In this paper, the research status on GEOMAS was described, the difficulties and challenges of GEOMAS were recounted, the advantages and disadvantages of synthetic aperture antenna system (SAAS) and real aperture antenna system (RAAS) were analyzed and the prospects for GEOMAS development were discussed.

Cloud cover is the main factor affecting the quality of remote sensing image. Cloud detection for remote sensing images is one of the principal problems that must be solved in remote sensing data restoration processing. On the basis of extensive investigation of existing articles, the research status of cloud detection is analyzed, and then a classification and comprehensive overview of cloud detection methods is presented, the cloud detection methods for several kinds of commonly used satellite data are also given. By comparing the cloud detection methods, the existing problems and development trend of cloud detection method are discussed.

The high precision matching of satellite images has been a problem of much concern. In this paper, the panchromatic images in the same track of GF-1 satellite PMS sensor are treated as investigated subjects. Based on the time-series statistical results of the offset property of the conjugate points, a self-adaptive image matching method is proposed considering mosaic imaging characteristics. In this method, the self-adaptive algorithm is added in the traditional image matching process. It can realize the self-adaptive selection of the target search window, search range and search direction using the iterative computation. The experiment results prove that the proposed method can achieve high precision self-adaptive matching of the GF-1 panchromatic images in the same track, which is useful for other similar satellites in future.

An improved RANSAC algorithm was proposed for point cloud segmentation and geometric primitives extraction of buildings with multiple facets and complex roof structures, including two innovations. Firstly, the “split-segment” strategy combined with regional growth concept is proposed to improve the segment result and efficiency of classic RANSAC algorithm; Secondly, an improved RANSAC algorithm with variant consensus set threshold is presented. By automatically adjusting the consensus set threshold value, geometric primitives with scale difference are likely to meet the validity test, thus avoiding the over-segmentation and under- segmentation problems of classic RANSAC algorithm with fixed consensus set threshold.

According to characteristics of GF-1 remote sensing images with high spatial resolution, the authors used compressed sensing theory to improve the traditional IHS image fusion algorithm. The component I from IHS transform and panchromatic images used sparse matrix and measure matrix, the weighted average and OMP yielded new component I'. Finally, through an inverse IHS transform the result image was obtained. Combined with five quantitative indexes, analysis and evaluation were conducted. Experimental results show that, compared with the traditional methods, IHS fusion algorithm combined with compression perception can obtain a higher and less distorted correlation coefficient, and the fusion results not only have higher spatial information richness, but also maintain the color information of multi-spectral images. It may provide a reference to GF-1 image visual solutions for translation and image classification.

As an important indicator, fractional vegetation coverage plays a critical role in the study of hydrology, weather, ecology and so on. Based on the Landsat8 as the data source and using Gramm - Schmidt transform spectrum sharpening fusion method, the authors improved the spatial resolution of thermal infrared band and calculated the thermal index. The traditional forest canopy density mapping model is improved based on the utilization of four indices to calculate the fractional vegetation coverage. A comparison with the traditional method shows that the improved model has a higher precision.

In order to remove the data redundancy of hyperspectral image and improve the accuracy and efficiency of hyperspectral image processing, this paper proposes a band selection method based on linear representation of hyperspectral image. A linear relationship is established for a band with the other bands, and the most relevant band is removed as a redundant band which is determined based on the multiple correlation coefficient. The set of minimum bands is finally obtained by repeating the above process for the remaining bands. It is proved that the set of selected endmembers by using the above bands is consistent with the set selected by using all bands, and the redundancy bands are removed to the greatest extent without affecting the endmember extraction. The experimental results show that the band selection algorithm in the paper is feasible and effective.

In order to improve efficiency and accuracy of remote sensing image retrieval, this paper proposes a remote sensing image retrieval approach based on granular computing model. Firstly, according to the tolerance granular computing theory, a series of concepts are defined, such as region tolerance granule, image tolerance granule and regional tolerance granular information table, and remote sensing images are granulated. Secondly, the region tolerance granular similarity is calculated. Finally, the remote sensing image similarity model is built combining tolerance granular computing and image integrated region matching algorithm. Using IKONOS data, the authors verified the two retrieval algorithms. The experimental results show that the precision of proposed approach is increased by 12.08% in comparison with original integrated region matching algorithm. Therefore, it can be concluded that the proposed approach can meet the users’ requirements.

The characteristics of high resolution SAR image is nonlinear and of high dimension. The description of SAR image in which a low dimensional manifold is embedded in high dimensional space is more useful for targets recognition. Therefore, a novel scheme of high resolution SAR image building area extraction is proposed by applying manifold learning to feature representation of a high dimensional SAR targets recognition. Firstly, the high resolution SAR image was preprocessed, and then eight texture features were extracted with gray level co-occurrence matrix (GLCM)so as to construct feature set with gray feature. Adaptive neighborhood selection neighborhood preserving embedding (ANSNPE)algorithm was used to extract the new features from the feature set. Finally, the building area was extracted by threshold segmentation with the new features and post processing, and the accuracy was evaluated. Selecting TerraSAR-X as test data, the authors carried out the experiments. The results show that ANSNPE algorithm can effectively extract the building area from high resolution SAR image, and has strong generalization capability. The projection matrix obtained through the training data can be directly applied to the new samples, and the accuracy of building area extraction could reach higher than 85%.

A method for rapid mosaicking UAV images is introduced, which is based on Voronoi network and GPU parallel computing. Firstly, the radiometric difference between the images is solved by Wallis dodging. Then the seamline network based on Voronoi diagram is created by the locations of all UAV images. At last, the UAV images are ortho-rectified and mosaicked rapidly based on the GPU parallel computing. Experimental results indicate that the method’s efficiency has increased by several times compared with the traditional methods by mosaicking 230 UAV images whose resolution is 0.1 meter. The mere rectification of the valid area of every UAV image and the application of GPU parallel computing not only avoid the huge data redundancy but also greatly reduces the computation time.

According to the super-resolution reconstruction of Jilin-1 satellite and on the basis of an analysis of different characteristics of satellite video and common video as well as remote sensing image,the authors studied the motion estimation applicability of the Vandewalle method and the method of LK(Lucas-Kanade)optical flow in pyramid under the situation of imaging scene containing moving objects. At the same time, according to the median shift and add(MSA)method, the new median shift and add(NMSA)method was proposed to tackle the problem that the edge information is not clear because of the lack of complementary information between the frames in the video satellite image reconstruction. First, a resolution grid is established based on the multiple of reconstruction, and the two grids are unified into a unified space. Then, the low-resolution pixel values that participate in estimation are determined. With the pixel of the image reconstruction grid to be determined as the center, the allowed error is used to determine the reconstruction pixel values. The experiments using the data of Jilin-1 satellite prove the effectiveness of the method proposed in this paper.

Hyperspectrum logging of core is one of the effective techniques for excavating geological data deeply and making a breakthrough in geological prospecting. Using hyperspectral remote sensing technology and deep rock sampling based on drilling technology has the advantages of mineral recognition. The first core scanner CMS350A in China has been developed successfully through the special project of national great scientific instruments and equipment, namely “development and marketing of core spectral scanner”. In consideration of the data collecting mechanism and characteristics of acquired core images and spectra by the scanner, the authors focused on developing the data preprocessing methods for the core image and spectrum data. A radiation correction method based on standard plate was developed for core scanning image, an interference spectrum detection and modification technology was proposed, and a model for automatic core image extraction and mosaicking was created to accurately process the data in time. These methods constitute the basis for core spectrum analysis, physic-chemical parameters inversion, and mineral analysis in future.

Sentinel-1A is a new generation SAR satellite on orbit. Its TOPS imaging mode is wide imaging mode, which can realize large-scale surface deformation monitoring. There are very rare researches on SBAS processing based on TOPS model images published in China and abroad at present. With the Yellow River Delta as the study area, 27 Sentinel-1A TOPS mode images from May 2015 to April 2016 were selected to generate 94 pairs of interferograms. Then, ECMWF meteorological data were used to estimate the atmospheric delay, which could achieve a wide range of atmospheric phase correction. Finally, the deformation and deformation velocity of the study area were extracted by SBAS timing analysis method. The results show that there is still a continuous settlement in recent years, and there is a significant sedimentation funnel in the vicinity of Qicheng industrial park, Guangrao saltworks and Nanzhaike Village in Guangrao Town. The results of settlement monitoring are consistent with the settlement rules described in the study area, and this proves the feasibility and effectiveness of TOPS model SBAS timing analysis method applied to deformation monitoring.

When remote sensing images are used to provide information for land cover mapping, it is negatively affected by the occurrence of mixed pixels in the remote sensing images, particularly in the case of coarse spatial resolutions. Soft classification and super-resolution(sub-pixel) mapping techniques can solve this kind of problems. The pixel-swapping(PS)algorithm is a simple and efficient technique for sub-pixel mapping. However, its computation is inefficient and yields poor mapping accuracy when the super-resolution scale factor (S)is large. A possible reason for this is that it only relies on the information from the fraction images. In this study, the digital elevation model(DEM) and their derivative data are employed as supplementary information for the PS algorithm so as to improve super-resolution mapping accuracy. Some conclusions have been reached: ① The sub-pixel mapping accuracy could be improved with the assistance of the DEM even if the scale factor is large; ② The mapping accuracy by incorporating both elevation and slope information is better than that of using elevation or slope data alone; ③ Mapping accuracy is less sensitive to the number of neighbors when scale factor is large;④ The computing efficiency is improved when incorporating DEM in pixel-swapping. Thus, it is feasible to use DEM as supplemental information for sub-pixel mapping.

Patch is the basic unit in landscape pattern and patch metrics constitute an important basis for quantifying patch morphology. Presently there are many patch level metrics and most of them have certain redundancy. Therefore for the purpose of applying patch metrics reasonably, the objectives of this paper are the applicability analysis of patch morphological metrics, the multiple-index analysis based on the patch morphological metrics and reducing many patch metrics to several patch metrics to express different patches. by the correlation analysis and principal component analysis. The experiment is on the basis of applicability analysis about 11 patch morphological metrics with global land cover data (GLC30), and all the metrics except area and perimeter are simplified by the two analysis methods. The experiment result shows that the two patch metrics, radius of gyration and support radius, could express and describe the different morphological patches well. The experimental results could serve as an important reference for the quantitative study of the scale effect in the landscape pattern.

Many mainstream segmentation algorithms for high resolution remote sensing image (HRI)rarely consider the segmentation quality in their region merging process. In order to solve this problem, this paper proposed a strategy to optimize heuristics with the purpose of enhancing segmentation accuracy of HRI captured over agricultural areas. Intra- and inter- region homogeneity models were firstly proposed, with the former constructed upon within-region spectral variance, and the latter considering edge strength extracted from multi-spectral and vegetation information. The criterion of the proposed heuristics was then constructed by combining the intra- and inter- region homogeneity. The new criterion enables the merging process to take into account the segmentation quality, thus constraining over- and under- segmentation errors effectively. Two scenes of HRI acquired over agricultural areas were utilized for validation experiment, and the performance of the proposed method was compared with other two newly proposed methods. By analyzing the quantitative evaluation of the segmentation results, it is found that the proposed method can remarkably improve the segmentation accuracy of HRI in agricultural landscape.

When blind signal separation technique is applied to unmixing hyperspectral data, a good initialization is vital for improving separating precision. Aimed at the hyperspectral data with relatively high spatial resolution and simple surface features, the authors put forward a reasonable hypothesis that the data contain pure pixel or approximate pure pixel corresponding to the each type of end-members, and proposed a new initialization method of non-negative matrix factorization(NMF), which has great potential in pixel unmixing. By calculating parameters to quantify the spectral shape and information difference among candidate pixels, this method extracts pure pixels from mixed pixels, recognizes the information dissimilarity among different types of pure pixels and choose the existing pixels that are most suitable for representing each type of end-members as NMF’s initial values. The experimental results show that the method proposed in this paper can improve NMF’s decomposition accuracy of hyperspectral data significantly, and its performance is better than that of other NMF initialization methods.

Unmanned aerial vehicle (UAV) low-altitude remote sensing is an extension and supplement of the traditional aerial photogrammetry, characterized by the airspace application convenience, short launch preparation time, and being less influenced by meteorological conditions, landing site restrictions and regional geological conditions. In order to promote the UAV low-altitude remote sensing technical application, the authors studied its key technologies. The function of UAV low-altitude remote sensing system and the factors considered in the design were analyzed, and the survey process was summarized. A complete technical route of UAV low-altitude remote sensing using in geological survey was formed. To prove the practicability of this technology method, the low-altitude UAV remote sensing system was built up for application in Zhoukoudian area. The results show that this means can provide timely and effective image for geological survey and emergence response survey, and has reference significance for low-altitude UAV remote sensing engineering application.

During the processing, image fusion, calibration, registration and other pre-processing steps for images are onerous tasks in land resources investigation. In this paper, the improved Welsh color transfer was proposed for image fusion. The study area was Xiantao City in Hubei Province and the study images were GF-2 high spatial resolution image data. The result of color-based image fusion algorithm was qualitatively and quantitatively compared with that of Gram-Schmidt(GS)and principal components(PC) fusion methods. The results show that the improved Welsh color transfer has good performance on preserving color fidelity and texture similarity. Especially in complex land-surface areas, the texture similarity of result image based on improved color transfer method is much better than that based on GS and PC fusion algorithm. The color transfer method is also applied to images fusion about different areas without calibration and registration for images.

Land use and land cover change (LUCC) is one of the important aspects in global change research, and it plays a key role in the regional environmental protection and economy by carrying out the study of regional spatio-temporal LUCC. In order to analyze spatio-temporal LUCC in the Fuxian Lake watershed from 1974 to 2014, the authors took a total of 10 periods of remote sensing images as the main data sources, consulted the first national geographic conditions census data, and acquired the LUCC information by using supervised classification and artificial interpretation from remote sensing images. At last, the authors further revealed the driving force mechanism by analyzing regional spatio-temporal LUCC in the past 40 years in the Fuxian Lake watershed based on socioeconomic data by principal component analysis and linear regression analysis methods. Some conclusions have been reached: With the increasing of human social and economic activities, garden land, housing construction area, roads and structures, artificial pile digging, a desert with bare surface increased significantly, while the cultivated land, forest land, grassland and water showed a trend of decrease in the Fuxian Lake watershed in the past 40 years. The LUCC changed significantly from 1990 to 1996 and 1996 to 2006, while the change was insignificant from 1974 to1977. From the result of transition of LUCC types, it can be concluded that the transitional ratio of forest land, grassland and farmland were more than other types of land, while water had steady condition. The LUCC is mainly affected by social and economic factors.

The Pearl River Delta urban group is a region with rapid economic development; nevertheless, with the economic development, the heat island effect becomes increasingly obvious. Contrast with traditional point surveillance, the thermal infrared remote sensing method can make us understand the spatial distribution of urban heat island more intuitively. In this paper, the authors retrieved the land surface temperature(LST)of the four seasons of the Pearl River Delta region using the split window algorithm based on MODIS images, and divided the urban core and suburban region based on impermeable surface area(ISA), and finally obtained the surface urban heat island intensity. The results show that the Pearl River Delta region has a serious heat island phenomenon, with the most severe season being summer and the weakest season being winter. There is a tendency that the connection of cities has led to the formation of large urban heat island, especially in the two most serious cities, Foshan and Guangzhou. The heat island intensity is negatively correlated with NDVI value and positively correlated with the degree of the city’s economic development. The research results would provide some ecological instructions for urban development planning of the Pearl River Delta region.

Aerosol optical depth (AOD) is a key indicator of the atmospheric environment, which can be used to represent the degree of atmospheric pollution. Obtaining AOD by satellite remote sensing can make up for the lack of the spatial distribution of AOD and the trend of overall change by ground-based observation. With Chizhou City as the study area, the authors retrieved AOD of four seasons in 2014 using the improved dense dark vegetation method(V5.2 method)based on EOS-MODIS data and analyzed spatial and temporal distribution of AOD after verifying its reliability. The research results show that there is a good consistency in the overall change trend between the AOD retrieved from MODIS and that from MOD04_3K, but they also have some differences, especially in the winter half year. There is a remarkable difference of AOD distribution both regionally and seasonally. Overall performance in the northern part of the AOD is significantly higher than in the south, and spring and summer seasons are higher than the autumn and winter seasons, while different regional trends are different. The temporal and spatial changes of AOD are strongly influenced by natural factors and human factors.

Water resource is indispensable to the growth of meadows in prairie, and the precise acquisition of the amount of precipitation is of great significance to the continental ecosystem and hydrological circle. This study proposed a novel technique to retrieve the precipitation on the basis of multi-temporal SAR imagery based on the fact that variations of the dielectric constant give rise to modifications of the soil moisture before and after the process of precipitation, allowing for the immediate changes in soil backscattering coefficients. Sentinel-1A SAR and actual measurements of rainfall in the meteorological stations of Erlianhot and Xilinhot were used to verify the retrieval results, which indicated the superb exponential regressive model between the difference values of backscattering coefficient before and after the process of precipitation and the real precipitation measurements. Thus, the spatial distribution of the retrieved precipitation on September 21, 2015 was obtained in the use of this method with Erlianhot as an example, meanwhile, the result shows the favorable spatial distribution consistency of the derived one in comparison with the product of MODIS atmospheric precipitable water considering the atmospheric motion and the acquisition time of imagery. Therefore, this pragmatic method is contrived to yield the actual rainfall spatial distribution in low vegetation coverage regions.

PM_2.5is the key air pollution for air quality of Kaifeng City. With remote sensing technology, the distribution of PM_2.5 concentration could be determined quickly. In this paper, the authors collected the aerosol optical depth (AOD) of GF-1, height of planetary boundary layer (HPBL), relative humidity (RH) and air temperature (AT) over Kaifeng City and then, with multiple regression analysis, revised the coefficients of all variables. After that, the authors built the PM_2.5 retrieving model from GF-1 in Kaifeng City. The validation from June to September in 2015 showed that the PM_2.5concentration from remote sensing was similar to that from four ground-level monitoring sites, and the correlation coefficient was higher than 0.8. The result of geographically weighted regression (GWR) was obviously better than that of no GWR. Nevertheless, when PM_2.5 concentration was high, the model would underestimate PM_2.5concentration.

The development of remote sensing technology provides strong support for monitoring the dynamic changes of the ecological environment. Shandian River is the ecological barrier of Beijing, Tianjing and Hebei Province. The eco-environment of Shandian River has aroused more and more attention among people, with the global climate change and human activities. In this paper, the authors used the remote sensing images obtained from 1985 to 2015 to study the changes of eco-environment of the upstream of Shandian River. Some conclusions have been reached: in 2015, the area of forest, grassland, unused land, water and cultivated land in study area was 413.75 km², 276.90 km², 1 779.94 km², 17.29 km², and 1 583.71 km² respectively, with the percentage being 10.16%,6.80%,43.72%,0.42% and 38.90% respectively. From 1985 to 2015, the forest area exhibited a downward trend, with the declining rate being 1.395 km²·a^-1. The grass land was declined too, with the declining rate between 9.47 km²·a^-1. The water remains unchanged in the past 30 years.

In the Loess Plateau region, it is difficult to extract the complex water automatically and accurately in a large area, and hence a new water extraction method is proposed in this paper, which combines the object-based image analysis and seeded region growing algorithm. In the first step, it uses object-based image analysis to extract the main part of the water body according to the different water features and form the seeds region of water area. Then based on the result, the seeds grew to the precise shape of water. Extraction result shows that the method is effective, high precise and high efficient.

As the vegetation canopy’s important parameter, the leaf area index (LAI) has important significance for crop growth monitoring and yield estimation. In this study, the authors used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye Oasis experimental area in Heihe River Basin as the experiment object and relied on physical and statistical model to estimate the inversion of the LAI. The process is as follows: First, the optimal linear regression model is established by using the normalized difference vegetation index (NDVI) and the corresponding measured LAI data. Then the physical model is adopted based on the combination of the mixed pixel decomposition model and the multiple scattering vegetation canopy model. With the linear regression model as the reference, the multiple scattering vegetation canopy model is modified, and the semi-empirical LAI inversion model is constructed. Finally, the fitting effects of the models are compared with each other. The results show that the semi-empirical model is the best model for LAI inversion in oasis area and its estimation accuracy of R² increases significantly to 0.89. This study provides technical support for the estimation of crop leaf area index in high precision, and will further promote the study and application of quantitative remote sensing theory about precision agriculture.

Multi-source satellite remote sensing data can provide rapid and accurate spatial information on blazes. According to the fire spot characteristics of sharply increasing in mid-infrared emissivity and brightness temperature, the authors analyzed the real-time dynamic monitoring of fire process, which happened in Jiuzhi of Guoluo Tibetan Autonomous Prefecture in Qinghai Province from March 18 to 19, 2016. Ten times of polar-orbit meteorological satellite data, especially NPP, were used to perform real-time monitoring, and GaoFen(GF) data were used to extracted the fire area. Finally, the fire distinguishing thresholds of Qinghai alpine grassland in spring were obtained. The results show that the fire-monitoring method could be applied to Qinghai, and that quick recognition threshold setting could meet the fast and effective requirements in daily real-time monitoring.

GF-1 is the first satellite in the major projects of high resolution observation system in China. It has provided abundant data for high and moderate spatial resolution image mosaicking for more than four years since its launching. The process of image mosaicking was based on the characteristics of the wide field of view (WFV)images of GF-1,including four key techniques i.e., data selection, geometric rectification, color adjustment, image mosaicking and mapping. The high precision geometric positioning of wide image is the key technology. After high quality data selection, ground control points (GCPs) were collected automatically. The root mean square error(RMSE)of each image should be less than 1 pixel with the average number of GCPs being 54. Then rational function model(RFM)was applied to the images for ortho-rectification. The 1: 5 000 000 national land image map was completed after color adjustment and mosaicking. This map has high spatial accuracy, colored pixels and temporal resolution. The method proposed in this paper could provide an important reference for the series of GF satellites image mosaicking and mapping, and would promote the domestic satellites to play a greater role in investigation of major resources and environment in China.

This paper, based on Landsat TM/TIRS between 1987 and 2015, deduced the surface temperature of the main city, conducted the normalized treatment of the land surface temperature, analyzed the evolution mechanism of the thermal environment in Wuhan City and discussed the characteristics of its diffusion model. The results show that the urban thermal environment has changed greatly in Wuhan City during the past 30 years. With the expansion of urban construction land, the diffusion pattern of the thermal environment pattern has evolved from the initial point-distribution to the stripe shape and zonal-distribution. The formation and evolution mechanism of the thermal environment in Wuhan City is closely related to the natural factors, such as solar radiation, atmospheric circulation and underlying surface properties, as well as the human factors, including the urban construction and development stage, planning policy, and so on.

The DMSP/OLS nighttime light(NTL)data provide an effective approach to characterizing the urbanization progress of cities, especially its spatial-temporal changes. In this paper,the authors combined the DMSP/OLS NTL data with the social economic statistics to examine the urban expansion of Hangzhou City, the capital of Zhejiang Province in eastern coastal region of China, during the period of 1992―2013. The relationship between urban expansion and traffic lines were also detected with the concept of landscape pattern and patches. The results show that the urbanization in Hangzhou City before 2000 was quite slow, and there existed an accelerated process since 2000 due to the development of economy, with the urbanization region also converted from central downtown to other areas along with the process. Further research indicates that the urbanization process in Hangzhou City has a great relevance with transportation in terms of direction and pattern, which might be attributed to the terrain and local condition. It is a novel attempt to investigate the urbanization of Hangzhou City on the basis of the DMSP/OLS NTL data and concepts of landscape, which would provide a significant guideline for the urban planning and development.

Since the GF-1 satellite was launched on April 26, 2013, a large quantity of data have been obtained, and the data volumes have entered into the multi-terabyte range, and even continue to grow. For effective management, retrieval and display of high-resolution remote sensing images, the authors take the efficient data storage model and faster browsing into consideration. The related experiments show that the storage and fast browsing of mass data can be realized by constructing the image pyramid by method of JPEG compression quality of 50% and method of quadratic interpolation, storing data with slices of 64×64, and creating GDB RasterCatalog Layer, which satisfies the need of industry-specific applications.

Based on an analysis of current mine remote sensing monitoring results data storage quality checking work flow, the authors have designed a results data quality checking system and implemented it for mine remote sensing monitoring, which realizes three major functions, i.e., data updating, data checking and rule customization. The plug-in GIS framework technology is used to realize the design, development, integration and test of the custom function plug-in, thus solving the problem of high coupling between the system and the functional module and greatly improving the system scalability. By using XML model-driven architecture technology, the quality inspection rule model is designed and integrated into the quality inspection system, which solves the problems of system upgrade and change of function requirements resulting from the change of quality inspection rules. Practical application shows that the system provides efficient software support for quality inspection of the storage, hence improving the efficiency of the work quality inspection and reducing the work intensity as well as the workload of the staff.

A plane split model was designed to resolve the problems of manual intervention dependency, low automation and difficulty in modification during remote sensing image mosaicking. Based on the GNU/Linux system, segmentation and mosaicking of experimental remote sensing images were carried out by Python and open source geospatial data abstraction library(GDAL). The set of algorithms was encapsulated by wxPython in the model view controller(MVC) framework. The design of the software with graphical user interface(GUI) is so perfect that it not only can be operated by GUI or command line, but also can process large numbers of images in batch. It has been well received to mosaic Landsat5 TM images in Land Cover Database Updated Program of Northeast China.