The presence of young stand cover alters the signatures that directly come from bare frozen soil surface.Matrix\|Doubling model is a complex passive microwave model to simulate the radiative transfer and attenuation properties of vegetation covers.In this paper,the Matrix-Doubling model is revised so that it can be used at frozen environment.The simulation results are then compared with experimental results obtained at Ku and Ka band by a truck-mounted dual-polarization microwave radiometer.In the end,the complex model (M-D model) was matched with the simple model (τ-ω model) under the same environment.In this way,the young stand effects on soil surface microwave radiation signal are eliminated.
High-precision height acquisition needs to correct the interferometric parameters errors by interferometric calibration for InSAR system.In order to reduce influence of the systematic random errors to the calibration precision,generally,while selecting GCPs,we follow the rule of minimum of conditional numbers of the sensitivity equations and choose GCPs along the whole strip of topography uniformly.but sometimes it's very difficult to satisfy this requirement as its hard to dispose GCPs in the field with complicated terrains,so this paper makes researches on the strategy of GCPs select and error transfer manner among calibration method for airborne InSAR with sparse GCPs based on bundle adjustment,and analyzed results show that if we can't fulfill the condition that all the GCPs uniformly distribute along the whole mapping strip while selecting GCPs,trying our best to make the GCPs disperse along the slant range direction and putting them near the far end of slant range direction is a better way.Meanwhile,when we make a joint calibration of several strips,the processing order should be from the far end to the near end in turn.Finally,in comparison with results from different strategy of GCPs select and processing order,we make a calibration to the simulative data of three strips from the highland and the results confirm the validity and rationality of the proposed strategy of GCPs select and error transfer manner among calibration method for airborne InSAR with sparse GCPs based on bundle adjustment.
Compared with the conventional radar altimeter,the synthetic aperture altimeter that uses the synthetic aperture technology in along track is a new-concept altimeter.As the delay compensation is corrected in the Doppler domain in the data processing,it is important to know the Doppler properties of the return waves.The formula about Doppler frequency is deduced from the relative velocity of the radar and the scatting point.As a result,the Doppler frequency from the earth rotation has an important effect on the echo waveform,measuring accuracy and effective number of look for satellite synthetic aperture altimeter.These must be considered in the delay compensation,system design and signal processing.
The synthetic aperture radiometer needs to complete correlations of many different channels.To solve the problem that the correlator unit of synthetic aperture radiometer is becoming so complex that it is not convenient for the implementation of spaceborne radiometer,a novelty design of digital correlator is given.Digital correlator is the main part of the synthetic aperture radiometer.The technology of new FPGA-Virtex5 and twelve hig speed analog\|digital converter AD9287 is used to complete correlation of twenty\|four polarization channels at 100 MHz sampling rate with 8 bit resolution.All the self\ and cross correlation calculations are processed at the same time in FPGA.The program can be easily erased to change the important parameters such as integration time.Structure of the system,interface circuit and software method are introduced in detail.The problem of bulk,power and complex correlation are all solved.Novel correlator with more sampling rate and more resolution can be designed based on the template given in the paper.
The North\|West Pacific Ocean is one of the places that have the maximal frequency of the Tropical Cyclones (TC) and the highest intensity of TCs in the world,especially to which intensity level up to typhoon.Typhoon is one of the disaster weather systems of serious influence the mankind produce living,while our country is one of the stricken serious country that is subjected to it.Typhoon brings the very great threaten and disasters to industry and agriculture production,traffic and transportation,the safe of life and property of people in cities of near southeast of sea in our country.It is significant to investigate the characteristics of TCs in the North\|West Pacific Ocean.With the development of detecting technique,nephogram became an important tool for monitoring tropical cyclone and typhoon in the tropical ocean,effective classification of image data and the segmentation of TC cloud are key steps for subsequent image feature extraction.Numerous scientific research had been launching into the area,compositive efficient algorithms were still in dire need.In the paper,the compositive orthogonal—matching pursuit algorithm,which was proved the efficiency in practice,and the compositive GA\|FCM (Genetic Algorithms and Fuzzy C\|Means algorithm) were introduced to segment the typhoon cloud systems.Although the veracity is needed to be improved,the algorithm could efficiency segment the main typhoon cloud systems,and can satisfy the forecast and monitoring of TCs well in the meteorological applications.
This paper introduced fast marching and minimum path into road extraction from remotely sensed images and proposed a new road extraction method.The work builds on a novel variant of fast marching approach,named as multi\|points fast marching.The underlying idea is to calculate minimum action map with multi\|points propagating simultaneously.Efficacious saddle points are selected with judgment criterion to exclude potential erroneous shortcuts.The extraction roads are continuous line features between couples of starting points that dispense with thinning and broken lines connection.The programming implementation of the method is given and remote sensing imageries of ETM+,IKONOS and airborne images have been taken as case studies.The test images have different spatial resolution,size and road network conditions.The experiment shows that multi\|points fast marching could extract roads from 161×163 ETM+ image in 0.10 s,1 653×1 630 IKONOS image in 21.20s ,1 579×1 698 airborne image in 20.80 s.The results demonstrated that the proposed method can extract multiple roads with high efficiency and little post-processing.
Currently,the principal component analysis,the ratio analysis,the spectral angle mapping method has been widely applied in mineralized alteration information extraction,and have got good results.According to the remote sensing data characteristics of the study area,this paper use the combinatory analysis of ratio and principal component,combining image processing method such as FLAASH atmosphere correction,median filtering and color density divide,to extract the mineralized alteration information of Qiemo region.The typical mineral spectrum of the USGS standard mineral spectrum library verificated that the combinatory analysis method can remove the data redundancy and noise, be convenien to obtain the alteration information.
In order to acquire the on-orbit value of interior orientation and improve the orientation precision of remote sensing image,a method for the on-orbit calibration of HJ1B-CCD camera interior orientation was studied with linear array push-broom sensor imaging model in this paper.Firstly,the ground control points are selected and colinearity equations are constructed between ground control points and relevant image points.Secondly,the error equations based on colinearity equations are built and the initial value of unknown parameters are estimated.Finally,the error equations are solved by special ridge estimation to acquire the interior and exterior orientation.The results showed that this method could calibrate the HJ1B-CCD camera interior orientation on\|orbit with good stability and precision.
Filter window selection is one of the key issues in SAR image despeckling.An adaptive windowing method for speckle reduction is proposed,which is based on the combination of confidence interval and morphological reconstruction.Pixel selecting is first proceeded in a fixed window based on the radiometric confidence interval.A confidence interval is chosen adaptively according to the homogeneity facts of current window.Then a region adjacency constrain is carried out by morphological reconstruction to refine the window to a radiometric and spatial continuous window with arbitrary shape.The experiment results show that the proposed adaptive windowing method performs better speckle reduction and structure preservation than the box filter and improved Sigma filter.
Texture has been widely used in remote sensing image classification to improve the classification result.In this paper,the recently developed texture measure Local Binary Pattern (LBP) was extended to a multivariate version to characterize the multivariate spatial correlation among multiple bands of multispectral image.The derived single\|band and multivariate texture features were then individually combined with the spectral data in image classification to evaluate the performance of the texture measure.Experiments demonstrate that compared with spectral classification,the classification accuracies can be significantly improved when the single\|band or the multivariate LBP texture features were included.The results also show that the classifications by incorporating multivariate texture show high overall accuracies,which are better than or at least comparable with the best classification result by adding the existing LBP texture;the use of multivariate LBP texture in image classification avoids the band selection procedure which is required in the incorporation of traditional LBP texture into image classification.
Aim at the defects of IHS (Intensity-hue-saturation) and wavelet transform fusion methods,an IHS transform fusion method based on the adjustment to wavelet low-frequency component is proposed by uniting the two methods,feasibility and superiority of the proposed method are verified through IKONOS and QuickBird images.Firstly,IHS transform technology is used to extract intensity from the true color image;following,in order to improve spectral fidelity of the fused image,combining intensity with Pan image,more reasonable intensity is produced by using wavelet transform technology;meanwhile,an adjustment to wavelet low\|frequency component is proposed and used to make high spatial frequency information gain of fused image better;finally,the true color fused image is obtained through IHS inverse transform.The study shows that the proposed method has the advantages of IHS and wavelet,the quality of its fused result is significantly improved.
ASTER-GDEM was a kind of data for global DEM and jointly developed by Japan and America.It was up to date with high resolution; SRTM3 was one kind of terrain data widely used by researchers.This paper describes assessment carried out over the ASTER\|GDEM and SRTM3 data.The study area was located in Jilin,China.First,the data parameters were analyzed in the ASTER-GDEM and SRTM3.Then the vertical accuracy of the data was assessed by using GPS points data.Comparison between ASTER\|GDEM and SRTM3 allowed a qualitative assessment of the horizontal component of the error,while statistical measures were used to estimate their accuracy.At last,slope percentages were calculated for different data.In the case,the accuracy of ASTER-GDEM was lower than SRTM3 at elevation,but higher at horizontal position,at the same time,it was rather different from the slope percentages of DEM made by digitize terrain map.The following conclusions were drawn from this study:ASTER-GDEM was appropriate for mountainous regions,and SRTM3 was suitable in plain area.
The classification algorithm of remote sensing image rapidly develops,as the classification accuracy of single classifier still cant meet the needs of practical application,the combined classifier becames an important aspect of the remote sensing classification.There are various types of combined classifiers.Compared with serial structure,Parallel structure is early studied ,fully developed and widely used.This paper analyzed classification accuracy of the combined classifiers based on parallel structure.The results indicated that the precision of combined classifier is related to the position of incorrectly classified pixels in each single.While the incorrectly classified pixels by single classifiers are separated ,the accuracy of combined classifier is the highest;while the incorrectly classified pixels by single classifiers are intersected in classified results,the precision of combined classifier is higher than that of each single classifier and the improvement is inversely proportional to the size of error set in combined classified results;while the incorrectly classified pixels by one classifier are included in another classifier,the accuracy of combined classifier is located in between the high and low accuracy,which is near to the higher one.This paper also experiments in Changsha local area,and the result effectively tested the deduction which we get in the analysis.Finally,this paper discussed theoretically the feasibility of improvement which combined classifier can get in the remote sensing image classification and provides an effective way to improve the performance of combined classifier.
In order to explore the feasibility of using Hyperspectral remote sensing technology to invert heavy metal Cu of red soil,based on the 350~2 500 nm band spectrum curve of 34 red loam samples.Through processing the spectrum curve and analysising spectral features,using three methods:spectral reflectance spectra,the first derivative and inverse\|log,the stepwise regression equations are established.And based on statistical method,analysising the relationship of soil chemical components and soil spectral features with soil heavy metal Cu.The results show that the soil heavy metal Cu was well correlated with the soil Fe and Mg,while had non significant correlation with soil organic matter.So claymineral had a significant effect on the content of Cu.The best relevance between soil spectra variables and soil heavy metal Cu appeared in band 830nm\,1 000 nm and 2 250 nm.And precision of the model for first derivative(79%) was better than that of reflectivity(66.26%) and of Inverse log(67%).The hyperspectral model of soil heavy metal Cu had potential for quickly prediction of soil heavy metal Cu content.
The objective of this paper is to reveal the relationship between the soil moisture content and Hyperspectral. The paper tested 60 sandy soil samples in the south of Gurban tunggut desert with SVC field\|portable spectroradiometer and established the liner regression between sandy moisture content and infrared bands which are sensitive to soil moisture content.The results showed that field test spectral of the sandy soil and logarithmic transformed refletance of the sandy soil did not show fine regression with the soil moisture content; the refletance which was continuum-removed and then first derivative regressed well with the soil moisture,the coefficient of determination (R2) was 0.855.This method provides a new idea to retrieve sandy soil moisture which is practicable and operated easily.
Leaf area index (LAI) is one of the key parameters for quantifying the energy and mass exchange characteristics of terrestrial ecosystems.To validate the feasibility of using HJ\|1A HSI data to retrieve leaf area index,HJ\|1A HSI data was acquired from China Center for Resource Satellite Data and Applications while in situ LAI was collected with a LAI\|2000 sensor.Based on the field experiment data,an evaluation of grass LAI retrieval methods was conducted using three vegetation indices including ratio vegetation index (RVI),normalized difference vegetation index (NDVI) and modified soil adjusted vegetation index (MSAVI).The results showed that the three vegetation indices derived from HSI data have good relationship with measured LAI.MSAVI gained the best result with R2 value of 0.622.While the cross validation result of MSAVI model was also permissible with R2 value of 0.547 and RMSE value of 0.202.All the above showed that the measured LAI and simulated LAI have good consistency.At last,the map of grass LAI,which could partially reflect the growing status of the pasture,was produced using HJ\|1A HSI data and MSAVI model.
This work present here was based on 12 images collected from two net stations (Landsat and University of Maryland) and combined with socio\|economic data to study urban expansion pattern,characteristic and driving forces variance of six counties and one city in Nantong area,which is located in the north wing of Changjiang (Yangtze River),and belongs to the famous Hu\|ning\|hang (Shanghai,Nanjing,Hangzhou) Industrial Belt,from 1979 to 2005.Landscape index,spatial and temporal dynamic analysis,and stepwise regression method were used to examine the change of each county and its driving force.The results demonstrated that: Landscape index analysis indicated that urban expansion in Nantong area has been undergone the key zone development to integrated development,from discrete to gathering.During temporal and spatial analysis,four types of urban development were categorized into,i.e.,Nantong,Hai’an and Rugao,Rudong,Haimen and Qidong and Tongzhou.Nantong has maintained rapid and the largest urban expansion in speed and quantities along Changjiang.Hai’an and Rugao had the largest changes in the past three decades,explicit trend to integrate in economic and other resources.Rudong,Haimen and Qidong illustrated larger changes,while Rudong expansion southward,Haimen eastward and Qidong westward.The driving force of those urban expansions divided into two types.One was driven by top\|down government\|oriented policy and population growth,while the other was driven by bottom\|up economic development improving people's living level.The population factor driving urban expansion may be further refinement.
Using the observation snow cover data from Landsat TM and ETM+ from January 2000 to May 2001,the inter annual temporal and spatial characteristics of snow cover over middle Tianshan mountains are analyzed.Combining with digital elevation model (DEM) data,the distribution of snow cover in different terrain conditions and different altitude per\|month are acquired.After analyzing the spatial distribution and temporal variation regulation of snow cover,it comes to a conclusion that the snow cover within year is correlated with altitude,aspect and slope.On the whole,the ratio of snow cover within year increases when the altitude increases and it decreases when the slope increases.The average height of snow cover boundary is high in summer and autumn but low in spring and winter.The difference of snow cover in aspect west and east is obvious in certain times,but the difference is less than that of the aspect north and south.This study provides a scientific support for utilization of water resource and the research of climate and environment in Tianshan Mountains.
Airborne Light Detection and Ranging (LiDAR) profile measurement technology is an advanced active remote sensing technology,including airborne LiDAR altimetry,and airborne LiDAR bathymetry.It can provide high resolution three dimensional topography,hydrography and forestry in large area,and has the advantages of accuracy,flexibility,efficiency,speediness.Airborne LiDAR profilometer can be used in fields as bathymetric surveys of large offshore areas and shorelines,coast ecological monitoring,the forest resource investigation and evaluation,and rapid response in natural hazard relief works and so on,has great application potential and broad development prospects.Firstly,a comprehensive summarization for the developments in recent years of airborne LiDAR profilometer abroad is presented,including LiDAR altimeter and LiDAR bathymeter,and comment on each phases is shown,and then the development status of this technology at home followed.Secondly,the main applications of airborne LiDAR altimetry and LiDAR bathymetry are discussed.Finally,the developing prospect in future of this technique is analyzed,wish to contribute to the improvement of airborne LiDAR profile measurement technology.
