The parallelepiped classifier (PC)，minimum distance classifier (MDC)，Maximum Likelihood Classifier (MLC)，Neural network (NN) and，especially，the newly developed Support Vector Machines (SVM) were assessed in the object-based image analysis of VHSR data. The impacts of kernel configuration on the performance of the SVM and of the selection of training data of the four classifiers were also evaluated. The result reveals that SVM can improve the accuracy significantly，and is by far more stable than other algorithms in the classification of VHSR data based on OBIA.

Land cover classification based on remote sensing image is of significant importance to agriculture, forestry and environment monitoring. Algorithm of remote sensing information retrieval is always an important research topic in this field. This paper made an effort to combine the Nonparametric Weighted Feature Extraction (NWFE) and texture features with the Support Vector Machines (SVM) so as to achieve a higher classification precision. The combined approach was applied to land cover classification of the Manasi River oasis in Xinjiang in 2006, and was compared with approaches of SVM based on Principal Component Analysis (PCA) and texture features and based on original bands and texture features. The results show that the method of SVM combined with NWFE and texture features can capture not only the distribution of land cover but also the difference among land cover types. An overall classification accuracy of 89.17% is obtained, which is better than those of two other classification results.

 If the classification type is unknown，the K-means algorithm will randomly select the initial values，and different initial values will lead to differences in remote sensing image classification results. To solve such problems，this paper proposes an improved K-means algorithm. First, logarithmical transform is performed for the original data，and then principal component transformation is implemented. The number of principal components for the K-means algorithm is determined according to the contribution rate (≥85%). The proposed method can weaken the noise. Kernel density estimation can be used to determine the probability density function of the first principal component, from which the initial label for multi-dimensional K-means algorithm can be efficiently determined，and the sensitivity of the initial value selected at random can be avoided.  Experiments show that the accuracy of the method proposed in this paper is higher than that of the traditional K-means based on mean-variance.

Based on comparing the capabilities of different water indices in monitoring water status of wheat, the authors selected the best indices for different periods. Using the data of wheat spectra and water status observed in a whole growth season of wheat, the authors calculated five popular water indices, i.e., NDVI, NDWI, GVMI, PWI and WI, and conducted a correlation analysis between these indices and EWT (Equivalent Water Thickness) as well as FMC (Fuel Moisture Content). The Results show that, in the early period of wheat growth, FMC is better than EWT in reflecting water status of the wheat, whereas in the late period, EWT is more suitable. Different periods have different best water indices, and the correlation between indices and water status tends to experience an increase in early periods and decrease in later periods. In the application, therefore, we should choose different indices for different periods.

A new method for extraction of built-up land information both in suburban area and in urban district by using Landsat TM/ETM⁺ imagery is proposed in this paper. Firstly, to suppress the information of bare soil with the middle-infrared(TM7), near-infrared(TM4) and green band (TM2), it is necessary to build a secondary index, which is called normalized difference bareness and built-up index(NDBBI). At the same time, to enhance the information of bare soil from existing indices of bare soil index(BSI)and modified normalized difference water index(MNDWI), another secondary index is built, which is called enhanced bare soil index(EBSI). Finally, the indices of NDBBI, EBSI, SAVI and MNDWI are applied to constructing a new index for delineating built-up land features in satellite imagery, which is called enhanced index-based built-up index(EIBI). The new index(EIBI)can be employed to extract the built-up land information both in suburban area and in urban district. This approach has been successful in Fuzhou and Zhangzhou experimental regions. Built-up features can be extracted objectively and sufficiently with the accuracy above 90%.

Currently, application-oriented researches on the data of Medium Resolution Spectral Imager (MERSI), which is on board China’s new generation polar orbit meteorological satellite FY-3, are very insufficient, due to the reason that the data as a new source have been delivered only since 2008. With the normal operation of FY-3 satellite system, it is necessary to develop an operational module for FY-3/MERSI regional land surface temperature (LST) retrieval and its post-processing, since LST is required for a wide variety of scientific studies but FY-3/MERSI’s operational LST products have not yet been provided by National Satellite Meteorological Center (NSMC). Based on an analysis of FY-3/MERSI L1 data’s HDF5 format and its channel characteristics, the authors selected the generalized single-channel algorithm developed by Jiménez-Muñoz & Sobrino to directly realize the LST retrieval at 250 m spatial resolution with MATLAB programming and the thematic mapping of LST derivative products. This paper describes the parametric processes of LST retrieval algorithm in detail, which include radiometric calibration, cloud detection, estimation of two intermediate parameters-surface emissivity and atmospheric water vapor,and calculation of thermal indexes from LST. On these bases, an automatic flowchart for FY-3/MERSI LST retrieval and thematic mapping was established. Experimental results of this flowchart applied in Shanghai thermal environmental monitoring show that it can process FY-3/MERSI L1 data in a fast, real-time and automatic way, thus suitable for operational products producing and sharing, with the saving of human resources. It is also proved that FY-3/MERSI data and various forms of LST products can reveal the spatial pattern of Shanghai thermal field and the urban heat island effect more finely and intuitively.

Land surface emissivity (LSE) is an essential parameter for land surface temperature (LST) retrieval from thermal remote sensing data. Up till now, three methods have been proposed for LST retrieval from TM6 data, namely, atmospheric correction, mono-window algorithm and single channel algorithm, which all require LSE as a parameter. In this paper the authors have first reviewed the three methods and then dealt emphatically with the estimation of this parameter. The method was applied to Lingxian area of Shangdong Province in North China Plain, the most important agricultural area in China, for LSE estimation and LST retrieval. The result shows that the method can yield a reasonable estimation of thermal variation of that area.

The false topographic phenomenon is a common phenomenon existing in the remote-sensing images obtained by sun-synchronous satellites, which brings great trouble to image users. In order to remove the false topographic phenomenon of the remote-sensing images，this paper put forward a method based on DEM data and IHS transformation and made a case study of the image of Guanyuan City in Sichuan Province. By low-pass filtering of the intensity image obtained by IHS transformation, the reflectivity information (IR) was extracted from the intensity image. By adding IR to shade relief image (SR), which was produced by DEM, a new intensity image (Inew) was obtained. A back IHS transformation was done to acquire corrected RGB image after replacing the old intensity image by the new intensity image (Inew). The experimental results indicate that the method can remove the false topography effectively and preserve the primary color.

Geometrical registration and threshold selection in remotely sensed image variation detection have been discussed in this paper. The detection of remotely sensed image variation was performed technically by using MATLAB. It is noticed that the general mathematical computing software MATLAB has been extended to a new application area, and a technical means for rapid development of remotely sensed image processing software has been explored.

 The methods for supervised and unsupervised classification of remote sensing images are reviewed in this paper. The main problems discussed include the merits, shortages and application fields and conditions of these methods. An integrated evaluation of these methods is also given. The future developing trends and the key points in the study of remote sensing image classification are dealt with at the end of this paper.

The lighted area, night light intensity, compounded night light index (CNLI) and total night light index (TNLI) of 16 cities in the Yangtze Delta were derived from the DMSP/OLS night light data in 2008, and their correlations with urbanization statistics were calculated respectively. Of these factors, the night light intensity proved to be an effective indicator for assessing the urbanization level. Construction land information of urban agglomerations in 1998, 2003 and 2008 was also extracted from DMSP/OLS night light data after the determination of the threshold value. Then the pattern and process of urbanization in the Yangtze Delta were analyzed and the expansion process of buildup area was categorized into several types. During the study period, urban agglomerations in the Yangtze Delta had three typical spatial expansion modes, of which the polygon mode could be observed in all the cities, the linear mode exists along main highways and railways, and the point mode occurs mainly around the less important cities. In spite of the imbalance of urbanization level within the study area, the regional differentiation was lessened gradually. According to the variation of the threshold value from 1998 to 2008, 11 cities, namely Zhenjiang, Changzhou, Yangzhou, Wuxi, Suzhou, Nantong, Huzhou, Jiaxing, Shaoxing, Zhoushan and Taizhou, belonged to the filling-in dominant type, Nanjing, Hangzhou and Shanghai belonged to the first-extension-then-filling-in dominant type, and Taizhou and Ningbo belonged to the first-filling-in-then-extension dominant type.

Object-oriented image analysis is the current research focus in information extraction,and the image segmentation method is the core technology of the object-oriented method. The effect of the segmentation directly affects the extraction of image objects. In this paper, the authors propose an object-oriented method for image segmentation which combines the advantages of edge detection using Canny operator with the secondary developing functions provided by eCognition Developer 8.0. Tests show that the segmentation method is accurate and reliable,the segmentation result is continuous and can well solve the "flood" and "broken" phenomenon. At the same time,this method that combines the advantages of the object-oriented method can satisfactorily solve the problem of "salt and pepper" and minimizes the impact of noise on the classification so as to extract the interesting object surface features.

In the rainy and foggy areas,the images acquired by GeoEye-1 satellite are often affected by haze. Because of the influence of the absorption,refraction,reflection and scattering role of the haze on the electromagnetic waves,the image clarity is decreased. Therefore,it is necessary to eliminate the impact of the haze on the image as much as possible so as to effectively improve the quality of images. Using ENVI software as an image processing platform,the authors selected the "homomorphic filtering" and "wavelet transform" methods commonly used in China and abroad and the "band calculation composition" method proposed in this paper to make a comparative study of the effects of haze elimination for the GeoEye-1 image. The results show that the use of "band calculation composition" to remove the haze from GeoEye-1 image has a good effect.

The thermal band data of Landsat TM (TM6) is highly suitable for analyzing the spatial patterns of the Earth's heat flux variation and surface temperature. Based on the thermal radiance transfer equation and several approximations to its terms, the authors developed a mono-window algorithm for retrieving land surface temperature (LST) from TM6 data. Unlike the conventional atmospheric correction which requires the in situ atmospheric profile data to estimate the atmospheric thermal radiance and absorption, the proposed mono-window algorithm directly involves the impacts of both atmosphere and the emitted ground into its computation, hence avoids the consequence of inaccurate atmospheric radiance estimate. The proposed algorithm requires two essential atmospheric parameters for LSTretrieval; transmittance and atmospheric average temperature. This paper discusses the estimation of the essential atmospheric parameters. Atmospheric effective mean temperature can be estimated using the ground meteorological observation data. Equations also have been constructed for estimation of atmospheric transmittance using the atmospheric water vapor content in the profile. Moderate errors in estimating the two essential atmospheric parameters may cause a bias of about 1.2℃ to the LSTretrieval, which is within the acceptable accuracy of 1.5℃ for most applications.

Based on analyzing the theory of the Optimum Band Combination, Principal Component Analysis (PCA) and NDISI, this paper presents an improved method, i.e., "experimental layer stack", to extract impervious surface of Taiyuan city, Shanxi Province, from Landsat TM image. Both unsupervised and supervised classification methods were used to classify the original multi-band image, PCA image, NDISI and experimental band combination images. The accuracies of the classification were assessed using 256 sampling points randomly selected from Google Earth high resolution image of Taiyuan. By comparison and analysis, the authors found that the experimental B combination method obtained the highest overall accuracy of 87.72% with the Kappa coefficient of 0.85.

ASD Field Spec Pro FRTM spectroradiometer was used to measure the spectral response of the vegetable and grass at different vegetation coverage levels. The data were applied to calculate six vegetation indices, i.e., NDVI (normalized difference vegetation index), DVI (difference vegetation index), RVI (ratio vegetation index), MVI (modified vegetation index), MSAVI (modified soil adjusted vegetation index) and GEMI (global environment monitoring index). Then the best combination of spectral bands was analyzed. Furthermore, the performance of different vegetation indices was investigated when they were used to estimate the vegetation coverage by using the dimidiate pixel model. The results show that, for the green vegetable, the best combinations of bands in the spectral region from 620 to 740 nm and from 780 to 900 nm have the best correlation with the vegetation index, whereas for the grass, the best combinations of bands are from 620 to 750 nm and from 760 to 900 nm, with the correlation coefficients of the two cases being all larger than 0.8. The bands of Landsat7 and HJ-1A CCD1 simulated according to the spectral response function were employed to calculate the six vegetation indices. The average overall accuracy for estimating the vegetation fraction by DVI and MSAVI is 83.7% and 79.5% respectively, indicating that they are superior to the other four vegetation indices as the input of vegetation index for the dimidiate pixel model.

The first national self-developed Hyper-Spectral Imaging (HSI) sensor was aboard on HJ-1A satellite which was launched on March 30, 2009 successfully. The HSI data quality was influenced by the stripe noise in the first 20 spectral bands severely. It is an urgent need to study the method to destripe the HSI data. In this paper, a new destriping method was developed based on an analysis of the main causes and characteristics of the stripe noise in the hyperspectral images. The method firstly devised a filter window to separate the random noise and stripe noise. Secondly, a look-up table between the gray level of each column and that of the standard column was calculated to destripe the stripe noise of the HSI data. The results indicate that this method can remove the stripe noise and random noise effectively and at the same time keep the spectral radiation information properly.

Aiming at reliable registration of remote sensing images,the authors present in this paper a remote sensing image registration method based on improved ORB (oriented brief) algorithm. The proposed method mainly includes three stages:The first stage is feature matching, the improved ORB algorithm is used to detect features and build descriptors,and the descriptors are matched to obtain initial control points. The second stage is to employ RANSAC (random sample consensus) processing via transformation parameters estimation to remove possible wrong matching points. The third stage is to rectify the image based on the transformation parameters calculated by the least square method. The proposed method is evaluated based on two sets of optical and SAR remote sensing images,and is compared with the registration methods based on SIFT and SURF algorithm. The results show that the method proposed in this paper can provide the same accurate remote sensing image registration result as or even the higher result than the methods based on SIFT and SURF algorithm,and can obtain improved efficiency.

Precision-farming is an essential part of modern agriculture. Precise management could be achieved by acquiring the field information on crops and their growing environment. Field management such as seeding, fertilizing, irrigating and harvesting could be optimized according to the spatial and temporal difference in crop and soil status. Along with the fast development of remote sensing technology, this technology has become an essential component part of precise farming and has been widely used in providing guidance for irrigation, fertilization, weed control, pest control and harvest. Remote sensing can be used to provide basic information of the field (field infrastructure and plot distribution) for farming management as well as to monitor dynamics of crop growing and such relevant environment factors as soil nutrition, soil moisture, crop nutrition and crop pest status in the field. The advances in applying remote sensing in these fields were reviewed and commented in this paper. Based on a review of the current application of remote sensing in precision farming, this paper describes in brief its research situation and gives a vista of its development. It is pointed out that improving the monitoring method, applying new data, integrating multi-source remote sensing data, integrating remote sensing data with agro and crop models and systemization are the key points in this field. Further researches on this field will promote the application of remote sensing in precision farming.

Compared with traditional survey technologies, mobile laser scanning has many advantages. Its characteristics make it possible to rapidly acquire large-area high-precision 3D spatial data for reconstruction of 3D (three-dimensional) model. This paper focuses on the classification of mobile laser scanning data. The authors present a multi-level classification method based on object feature extraction, namely extraction of main features by PCA(Principal Component Analysis). This method was applied to blocks point data obtained by mobile laser scanning, and the results show that the proposed classification method is promising.

Although composite data present lower atmospheric contamination than raw time series, MODIS NDVI products are still contaminated by clouds, especially when cloud cover lasts longer than the composite period. e.g., in the rainy season. The long-time cloud cover will weaken the application of MODIS NDVI time series data. To remove the effect of these clouds from NDVI data and reconstruct high-quality NDVI data, the authors propose three algorithms for cloud removal, namely SPLINE function, HANTS and Savizky-Golay. The capabilities of the three algorithms in cloud removal was compared with each other in this study, with the MODIS NDVI time series data in Shandong province serving as the test data. The results show that the three algorithms can remove the effect of cloud from NDVI time series data effectively, with each algorithm having its own advantages and disadvantages. For the algorithm of SPLINE function, the result of cloud removal mainly depends on the quality of cloud data and sometimes extreme values will occur;  this algorithm fails to change the values of pixels which have not been contaminated atmospherically. When HANTS and Savizky-Golay algorithms are used, most of the pixels will lose their original values, and the parameters have to be determined after conducting many experiments because there is no objective rule to set them.

Impervious surface, as an important indicator to measure the urbanization degree and environmental quality, has attracted more and more attention. The magnitude, location, geometry, spatial pattern of impervious surfaces and the ratio of perviousness-imperviousness significantly affect regional eco-environment changes. Extracting and mapping impervious surface by means of multiple remote sensing data and analytical methods have constituted a hot topic in these research directions. In this paper, impervious surface extraction methods are summarized from traditional method of remote sensing, extraction based on spectrum and geometrical features and artificial intelligence algorithms, then the principles, characteristics, application fields are described, and finally the future prospects are pointed out.

 Hydrocarbon micro-seepage is a common phenomenon over oil and gas reservoirs. Remote sensing data are important sources in extracting seepage information for the exploration of oil and gas resources. This paper presents the theory of hydrocarbon micro-seepage and describes different manifestations of hydrocarbon micro-seepage at continental surface, in offshore area and on sea surface. An overall discussion is given in this paper concerning the research progress and the development trend of the remote sensing technology in China.

 

An approach to land use change detection by using high resolution remote sensing images is put forward in this paper. With the help of GIS land use map, image objects can be obtained by the matching of land use map and remote sensing images in the same region. Meanwhile pixel-based supervised classification is conducted for each image so that each pixel has its own class code. Then image subsegments can be obtained based on the image segment and the class code of each pixel within it. Image subsegments can be regarded as the basic units for feature extraction. Correlation coefficient is used for detecting changes between the images gotten from different time periods, and instead of the empirical selection, the change threshold is founded automatically by using ROC curve (receiver operating characteristic curve). Two multispectral Quickbird images obtained in 2002 and 2005 respectively and a 1:10 000 land use map of 2002 in the same region were used in the experiment. This study area is located in Wuhan City and the result shows that most land use changes can be detected, and hence this approach is effective.

 NASA MODIS sea ice products provide global information of sea ice extent and ice surface temperature (IST). However, when it is used for local and regional sea ice mapping, its accuracy needs further validation. Taking sea ice in Liaodong Bay as an example, the author analyzed MODIS sea ice products, and the result shows that almost all the sea ices in this area are identified as clouds. Based on NASA sea ice algorithm, the author extracted sea ice extent by using sea ice reflectance and ice surface temperature respectively. The results show that ice surface temperature can extract the distribution of sea ice effectively.

Community remote sensing (CRS),a new approach to the application of remote sensing,is a human-based remote sensing technique. With activities of volunteers, this technique can compensate effectively for the disadvantage of traditional monitoring equipment of remote sensing. Based on abundant literatures of related topics,the authors described the status of the development of CRS that emerged in recent years,and discussed its potential for geoscience applications in such aspects as primary research contents,adopted techniques,application field and future prospect. The research indicates that, although the application of CRS has just started and is met with many problems,the carried-out-projects and applying practices have fully proved its great potential. In recent years,CRS has achieved important progress especially in such fields as ecosystem monitoring and disaster response. Its geoscience application, however, is relatively limited,showing a great space of developing in the future. CRS,as an emerging field of geoscience applications,might affect the future decadal development of new approaches to geosciences applications and hence deserves much attention.

An automatic geometry registration method based on Features from Accelerated Segment Test (FAST) corner detector and Speeded Up Robust Features (SURF) is proposed in this paper. Firstly, applying HSI transform on both the reference image and the image to registration, and then building gauss pyramid of the images. Secondly, detecting and extracting the FAST corner points of both images, and calculating the SURF descriptors of the corner points, following by searching match point pairs by K-D tree. Thirdly, iteratively using partial minimum least squares to remove error point pairs and then calculate the geometry transform coefficients. Lastly, excuting the geometry transform to get the registration image. An experiment on two groups of images was performed, in which the proposed method was respectively compared with the automatic registration method based on SURF features and the method used in ENVI software to obtain ground control points automatically, and the results show that the proposed method can get more match points and obtain higher geometric accuracy, except which is slightly inferior to SURF algorithm in scale invariance.

: Water pollution in the lake always occurs along with eutrophication. Strong correlation has been found

between such water quality parameters as chlorophyll-a, SD, TP, TN, COD and BOD5 in the water body. TP and TN

directly control the growth and propagation of phytoplankton, and theoretically speaking, TP and TN affect

chlorophyll-a and SD indirectly, whereas chlorophyll-a and SD are primary parameters that can influence spectral

reflectance characteristics. Previous research results indicate that retrieval models of TP and TN can be developed

directly using remote sensing data because of their strong correlation with chlorophyll-a and SD. Based on this

technical idea, the authors derived the BOD5 retrieval model in the Hongze Lake using TM images according to its

strong correlation with TP and TN. The result demonstrates that the model is simple and feasible. This study proves

to be a successful experiment in the construction of the retrieval model of water quality parameters.

 In order to overcome the deficiency of sampling methods for SRTM and ASTER, the authors constructed a flow chart for SRTM and ASTER efficient fusion, with the DEMs of Dongzhi tableland as an example. Firstly, ASTER and SRTM were respectively transformed from spatial domain to frequency domain in terms of Fast Fourier Transform (FFT). Secondly, the ideal high and low pass filters were respectively employed to remove their low and high frequency errors. Thirdly, the filtered frequency domains were added up. At last, the summed frequency domain was transformed to spatial domain in terms of inverse FFT. The results indicate that, compared with errors of ASTER and SRTM, both the minimum and maximum errors of the fused DEM show an obvious decrease, the RMSE has a weak decrease and the number of the errors bigger than 30 m is much less than that of SRTM and ASTER.

Above-ground biomass of forest has great research and application value in the forest ecological system. There are mainly three types of models for estimating above-ground biomass of forest, i.e., forest measuring method, remote sensing method and integrated method. Remote sensing technique has become an important means for obtaining above-ground biomass of forest at the regional scale. There are mainly four types of remote sensing models, namely empirical, ANN, physical and NPP based models. This paper has analyzed and discussed the present methods for estimating above-ground biomass of forest based on remote sensing as well as their advantages and disadvantages. Finally, this paper points out that the integrated method combining remote sensing technique and forest succession model can be generally used to estimate above-ground biomass of forest at the regional scale in future.

A new cloud removal algorithm based on spectral and geometric information is proposed for generating cloud free and cloud-shadow free mosaic image from multi-temporal TM images. At first,single image and multi-temporal images of thick cloud and cloud-shadow multi-spectral detection models are built based on TM spectral characteristics analysis. Secondly, cloud is detected according to spectral characteristics,and then a technique is applied based on coupled geometrical relationship between the cloud and its shadow by using sun azimuth angle, sun elevation angle and statistical distance between cloud and its shadow so as to automatically predict the approximate location of cloud-shadow. After that, erosion filtering and dilation filtering are used sequentially in the cloud and cloud-shadow fraction image to eliminate the small bits and generate the exact zones contaminated by cloud and cloud-shadow. At last, the cloud and cloud-shadow zones of the target image are replaced by the same-location cloud-free zones on reference images whose spectral information is matched with the target image by the linear regression method. The results show that this algorithm is capable of eliminating the cloud influence from TM image significantly. Moreover, it can effectively eliminate the influence of water body and hill shadow on cloud-shadow. This method can therefore support producing cloud removal images in a quick, simple and applicable way.

To further understand the ecologic restoration level after water supplying，the authors studied vegetation coverage （Fv）in the water supply area of the lower reaches of the Tarim River. Correlation equation and two-dimensional scatter plot between Fv and vegetation index （VI） could be obtained based on MODIS data by constructing a variety of VIs in combination with measured Fvs，and the value of Fv could be figured out based on the relevant equation. The results show that there exist good correlations between the VIs such as NDVI，MSAVI，SAVI and EVI and Fvs on the basis of MODIS data. With these VIs，the inversion accuracy of the Fvs in the order from higher to lower is NDVI, EVI, MSAVI and SAVI. The authors suggest using MODIS data to monitor vegetation change in the arid desert area，with the optimal choice of VI being NDVI.

Through analyzing the annual volatility of cloud detection thresholds and integrating the band characteristics of HJ-1B,the authors proposed a new algorithm of cloud detection for HJ-1B image based on dynamical thresholding according to the spectral standard deviation anomaly. Using image registration,band math,linear regression and error analysis,the authors acquired the cloud abnormal regions which could be used to remove cloud pixels from the image. The results show that this approach can detect cloud pixels over different periods and in different scenes successfully,thus promoting the use of HJ-1B data and improving the precision of image classification.

Based on an analysis of mineral composition and spectral characteristics of samples collected in Bulunkou area of West Kunlun Mountains,the authors extracted the end-member spectra of lithologic units by using ASTER data of the study area,and the ASTER VNIR and SWIR data of the study area were processed by matched filtering method, and then nine important litholigic units in the study area were extracted. These lithologic units include biotite quartzite,biotite plagioclase gneiss,biotite quartz schist and biotite hornblende plagioclase gneiss which belong to Palaeoproterozoic Bulunkuole Group (Pt1) and chlorite sericite slate,black phyllite and sericite quartz schist that belong to Silurian Wenquangou Group (SW) as well as quartz diorite and tonalite. An analysis of geological data available and field survey results demonstrate that the method adopted by the authors can extract credible information and supply references to lithologic mapping and mineral exploration.

 The extensive distribution of geomorphic surface is the main geomorphologic characteristic of Guizhou and its adjacent areas，and the detailed study of this characteristic is very important in reconstructing Cenozoic evolution. Taking Guizhou and its adjacent areas as the study district， based on ASTER-GDEM data (30 m)  and using ArcGIS platform，the authors extracted the geomorphic surfaces with slope data and statistical method，and obtained the precise quantitative features of the geomorphic surfaces in this district for the first time. The results provide a new insight into the study of step-like landforms. It is shown that there are 4 steps of geomorphic surfaces in the study district， which suggests that Guizhou and its adjacent areas has experienced at least 3 times of complicated tectonic uplift since Cenozoic with the uplifted elevation being 300～500 m each time, and remain in the process of uplifting at present.

To deal with the Level 2 HSI data from the newly-launched HJ-1A satellite, this paper introduced in details the entire flow and relevant algorithms for data preprocessing. The introduction includes calibration, vertical stripes elimination, and atmospheric correction geometric correction. Standard spectral reflectance products with precise geo-locations were produced. Spectral reflectance data from EO-1 Hyperion of close dates was used to simulate the band reflectance of HJ-1A HSI. Comparisons of spectral reflectance data between simulated and actual HJ-1A HSI were made to validate the effect of the data preprocessing. The average correlation coefficient of spectral reflectance between actual and simulated HJ-1A HSI is 0.947 with its standard deviation being 0.017, suggesting a high consistency. The mean and standard deviation of differential bands between real and simulated HJ-1A HSI are close to 0. The result shows that the reflectance from HJ-1A HSI is consistent with that of simulated data from Hyperion, and hence the data processing flow could provide necessary support for quantitative use of HJ-1A HSI data.

 The radiometric calibration parameter is very important in quantitative application of remote sense data. In this paper, the Landsat Thematic Mapper Data of Beijing acquired in April 26, 2007 were applied to evaluating the radiometric calibration parameters supplied by USGS and RSGS. First, the radiance data were computed respectively with the calibration coefficients converted from calibration parameters.Second, the FLAASH atmosphere correction model was used to compute meteorological range and surface reflectance.Third, according to the aerosol observation data from AERONET and high resolution remote sensing image, the accuracy evaluation results of the two radiometric calibration parameters were obtained on the basis of the accuracy of meteorological range and classification of surface reflectance. The results show that parameters supplied by USGS can reflect radiometric features of TM sensor more accurately.

 As the surface of the manganese ore deposit always contains a large amount of hydroxyl and ferric ions，manganese mineralization anomaly can be characterized to some extent by the hydroxyl and iron anomaly. The authors therefore proposed the adoption of such methods as band ratio，threshold segmentation and principal component analysis to extract the hydroxyl and iron abnormal alteration information from ETM+ remote sensing images，and analyzed the extracted abnormal alteration information in combination with data from known ore spots by using GIS. With manganese deposits in western Guangxi and southeastern Yunnan as examples and through the manganese mineralization alteration information extraction and comprehensive analysis，the authors verified the relationship between manganese mineralization and remote sensing alteration information as well as the effectiveness of the methods used in this paper and, on such a basis, put forward new ideas and methods for the prediction and evaluation of manganese ore deposits. Using extracted alteration information，the authors divided the study area into three kinds of favorable ore-forming zones on the basis of ore-forming favorable degree and delineated six potential districts for ore-prospecting in this area.

The spatial variation of Chongming Dongtan wetland was monitored using 10 remote sensing images including Landsat MSS, TM and ETM⁺ data. Nine nautical charts were employed to analyze the evolution of underwater terrain from 1951 to 2006. With the above/under water topographic changes as an entirety, the authors made quantitative and qualitative analysis of geomorphic evolution in the past 60 years at Chongming Dongtan wetland using waterline method and depth contour overlaying. Some conclusions have been reached: ① The dynamics of Chongming Dongtan tend to be stable, characterized by erosion in the south part, deposition in the north, rapid propagation in the middle and alternate erosion and deposition in some coasts under the stabilities of the current Yangtze estuary; ② Human activities made important effect on the wetland. Especially, damn in the Baigang tidal channel accelerated deposition in the wetland. Then the 0 m isobath expanded to the east at a stable rate. Although the wetland area was increasing with the human reclamation year by year, the inter-tidal structure of wetland was deviated from the nature state. The proportion of high tidal flat was decreasing and it showed discontinuous spatial distribution at Chongming Dongtan. Because the damn was directly exposed to the outside marine environment, it would reduce the capability of disaster weather prevention.

Automatic generation is a critical procedure in remote sensing image mosaicking and a key technique for producing digital orthophoto map (DOM). The seamline is closely related to the object integrity and color consistency in the image,so the automatic generation of seamline determines directly the quality and efficiency of mosaic processing, which thus becomes a hot research topic both in China and abroad. In this paper,the authors described the relative principle of the seamline generation and made a summary of four methods for automatic generation of seamline,which included methods based on overlap area image difference,identical points,auxiliary data and morphology. The prospect of further optimizing the seamline is forecasted on the basis of comparing and analyzing the advantages and disadvantages of these methods.

The lightness values of three types of water, i.e. ordinary, eutrophic and seriously polluted, and vegetation in the shadow of the hill were analyzed in this paper. The results show that the lightness value of TM4 is lower than that of TM3 for ordinary water, whereas things are opposite for vegetation in the shadow of the hill; the eutrophic water contaminated by phytoplankton has strong reflection in TM4, and seriously polluted water has strong absorption in visible band, which the lightness value of TM4 is higher than that of TM3. Thus the eutrophic and polluted water couldn’t be distinguished from vegetation in the shadow of the hill by comparison between TM3 and TM4. According to the extraction method of classification of water quality type the spectral characteristics of the water,the authors set up the thresholds to distinguish various types of water quality, vegetation in the shadow of the hill and other ground objects,and extracted the water distribution information from TM imagery quickly,accurately and efficiently.

2D scatter plot of remote sensing image,which is the best graphical representation method,expresses the structural information of the combination of two bands in spectral space; nevertheless, previous work only focused on such problems as the background - abnormal subspace model in the dot matrix space of spectral data,the estimation of the elliptical geometry parameters of 2D scatter plot based on the Gaussian distribution model of combination of 2 bands’ probability density,and the relationship between the background,interference and alteration anomaly of remote sensing image to the spatial structure of 2D scatter plot. Further studies show that the type classification of 2D scatter plot of remote sensing image is based on the physical meaning,and it can not only locate the background,interference and alteration information of the remote sensing image but also direct the selection of the method for the extraction of the remote sensing alteration information. At last,the authors discussed different kinds of 2D scatter plot practically used in the extraction of the remote sensing alteration information and,with practical examples, explained the theoretical basis and the application results of the 2D scatter plot used in the extraction of the remote sensing alteration information,which is based on the physical meaning.

Registration between remote sensing images and DEM data is difficult because it is hard to find the Ground Control Points (GCPs) between the two images. In this paper, an illumination model was introduced and served as a proxy in the image registration to increase the registration accuracy. The aspect and slope were computed for each pixel of the image based on DEM data. The solar elevation angle and azimuth were obtained from the header file of the remote sensing image. Based on these parameters, the authors constructed the terrain illumination model based on the Lambert reflectance model, which displays very similar texture to the remote sensing image. Thus, the GCPs can be easily identified in the two images. This method was tested by using sub ETM+ image and DEM in Guangdong province. The result shows that the total RMSE of this method is 13.373 m, which is less than one pixel. This method is effective in the registration of remote sensing image and DEM, especially in mountain areas.

Using GIS technology as well as theory and methods of landscape ecology，the authors studied landscape pattern of land consolidation. Exemplified by the land consolidation key project for Huainan city，the authors selected three targets and probed into the land use situation and landscape pattern before and after land consolidation. The changes of land use structure and landscape patterns based on land consolidation were analyzed using Geographical Information System combined with the method of landscape ecology, and some results were obtained. First，paddy fields are predominant in the landscape pattern, and the patch fractal dimension and the shape parameters tend to decrease gradually, indicating that the pattern of patches has become regular and simple. Second，the number of landscape patches and patch densities are relatively low，the average patch area and the maximum patch area increase，and the maximum patch index rises steadily. All this shows that landscape diversity has been reduced simultaneously with the increase of the landscape distribution uniformity.  

In the past decades, researchers have successfully rebuilt the digital elevation model (DEM) using such Interferometric synthetic aperture radar data (InSAR) as SIR-C/X SAR and ERS1/2. As a new generation of synthetic aperture radar, Phased Array type L-band Synthetic Aperture Radar (PALSAR) , which is onboard Advanced Land Observing Satellite (ALOS), works at a longer wave length-L band. Its penetrating depth is deeper than the radars that work at C band. Thus it has advantages in the construction of DEM. However, there have been few reports about the DEM extraction from this technology. The open source program-package ROI_PAC version3.0 provided by NASA/JPL can be used to rebuild DEM from PALSAR Level 1.0 data that is not calibrated. Therefore, ROI_PAC version 3.0 was modified in this study to make it rebuild DEM from PALSAR Level 1.1 data. The workflow of ROI_PAC was described. The method introduced in this paper was validated by a set of PALSAR Level 1.1 data. A comparison between InSAR DEM and reference DEM was made. The difference between them is 0.27 m, with a standard deviation of 9.24 m. There are more than 80% pixels having height errors within 10 m. The results show that the method proposed in this study is useful.

In view of the nonexistence of geometric model for epipolar image, this paper, based on the RPC model, puts forward a practical way of building geometric model from epipolar image generated by the projection track method. With the support of across-track stereo image data of SPOT 5 HRG, CBERS 2-03 and along-track stereo image data of SPOT 5 HRS, P5, GeoEye, IKONOS, this paper indicates that the RMS of vertical parallax of the epipolar image is within 0.2 pixel,  that the planar RMS calculated from forward intersection by the epipolar model and the original image model is within 1m, and that the altitude RMS is within 0.3 m.

The microwave land surface emissivity(MLSE)is a very important parameter for describing the characteristics of the lands, and it is also a key factor for retrieving the parameters of land and atmosphere. Space-borne passive microwave radiometers provide direct retrieved land surface emissivity spectra with larger temporal and spatial scales compared with physical modeling simulation in that the physical modeling simulation needs plenty of parameters, but quite a few of these parameters, such as parameters of land surface and vegetation, are not available from traditional measurements. This paper systematically reviews MLSE retrieving algorithms for passive microwave remote sensing data, which include statistical approach, atmospheric radiation transfer model approach, index analysis approach, neural network approach and one-dimensionally variational analysis approach. The main advantages and limitations of these five methods are also discussed. Finally, the development tendencies of estimating MLSE by remote sensing are pointed out, such as developing algorithms of Radio Frequency Interference (RFI) detection and correction, improving algorithms of detection of clouds and rain-affected radiances, and intensive research on microwave atmospheric radiation transfer process.

To tackle the problems existent in road information extraction from high resolution remote sensing, the authors put forward an improved approach to road extraction based on watershed segmentation according to the basic theories of object-oriented method and mathematical morphology. Firstly, the image is processed by improved watershed segmentation to extract basic road information after preprocessing. Then object-oriented method is used to extract road per-parcel so as to optimize the road extraction results. Finally, after binary image processing, the incomplete results can be removed and corrected by using mathematical morphological transformation. Experimentation shows that the proposed method can extract urban road information efficiently and process the roads from the complex urban context fairly satisfactorily.

The Duobuza deposit in Gaize of Tibet is one of the large-size porphyry copper deposits in Bangong Lake-Nujiang River mineralization zone found in recent years. The study of this typical deposit can provide good understanding for mineral deposit prognosis of this mineralization zone. In this paper, the spectra of samples in the Duobuza deposit were measured by using USA TerraSpec spectrometer, and at the same time, the microscopic identification of the samples was conducted. The results show that the main alterations in this deposit are phyllic alteration and epidotization, and the main mineralizations on the surface include limonite, malachite and azurite. The spectra of two kinds of granodiorite-porphyry are different. The spectrum of granodiorite-porphyry related to mineralization shows distinct phyllic alteration, and the spectrum of granodiorite-porphyry which is not related to mineralization shows little or non alteration, distributed around the first kind of granodiorite-porphyry. The spectra of the samples measured by using TerraSpec are consistent with the spectra of ASTER image. Finally, the phyllic alteration area which has close relationship with the mineralization was delineated based on the spectral analysis, and these areas are considered to be more important prospecting areas in the future.

This paper proposes an object-oriented point clouds classification method for solving the difficult classification problem for the overlapping between vegetation and buildings based on reviewing current status of LiDAR point clouds classification approaches. In the proposed method, the point clouds are firstly separated into ground points and non-ground points through adaptive TIN filter method, and the DTM is obtained. Second, a triangle network is constructed for non-ground points higher than DTM. The non-ground point clouds could be divided into multi-objects by removing longer edges (edge between ground and object). Then, the object is judged to decide whether it belongs to vegetation or building according to its information entropy of triangle network slope. Finally, for objects difficult to be distinguished from other objects, the overlapped area between vegetation and buildings is extended by geometric shape of buildings, so that the accuracy of point clouds classification of the overlapped area could be improved. The experiment results show good classification performance for buildings and vegetation, and the accuracy reaches 87%.