最新刊期
卷 18 ， 期 6 ， 2014
- 摘要：Contemporary remote sensing science is challenged by serious supply-demand contradiction between data acquisition and application capabilities. Scale issue is attributed as a major and essential problem retarding the development of remote sensing in both theories and applications. Due to heterogeneity and complexity of land surface,scale issue naturally arises as how remote sens-ing observations match phenomenon or process on earth surface or how multi-scale remote sensing observations maintain c onsistency. Therefore,this paper endeavors to build up a universal scaling methodology framework,which would hopefully help interpret and transfer remote sensing products between sensor-defined scales and user-demand scales.To begin with,scale issues in remote sensing are analyzed as: inconsistency between pixel-based remote sensing observation and conventional point-based field measurement,inconsistency between remote sensing products at varied scales,scale adaptabilities of physically-based models and scale inconsistency between remote sensing product and user demand. It is pointed out that multi-scale remote sensing information is related to characteristics of land surface and simple averaging or classic image processing will result unexplainable data.Second,scaling methods are explored in terms of up-scaling and down-scaling. Up-scaling involves scaling from point-based field observations to pixel-based remote sensing observations and scaling between remote sensing products at varied scales. For the former a general way is to introduce extra information while the latter always uses two approaches as clumped or distributed c omputation. Preferred up-scaling is supposed to reserve key information,but most current methods tend to derive more homogeneous product of low information quantity. Down-scaling refers to derive data with more detailed information. The insufficiency of information of the original image is unavoidable and it is mandatory to introduce extra information. The earlier downscaling methods largely rely on pre-defined assumptions or interpolation,while a recent trend is to use relevant geographic features as prior knowledge. The central issue in scaling is concluded as how to reserve key information or how to introduce extra information.Third,borrowing ideas of prior knowledge from quantitative remote sensing retrieval and trend surface in geographic applications,the paper proposes the concept of geographic trend surface and establishes a universal scaling methodology. The core is:（ 1） to construct prior knowledge warehouse by integrating all necessary information in retrieval and scaling,including link model,spectral knowledge,geographic trend surface,recent temporal change records and process models;（ 2） to adjust the geographic trend surface in spatial and temporal frame under the support of prior knowledge warehouse. All kinds of geographic information,including temporally stable geographic features,temporal change records,field observations and process models,are used to derive the spatial patterns of the target feature,based on which the spatial representativeness of observation at each location can be identified;（ 3） to update prior trend surface to post trend surface through Bayes theorem,where prior trend surface is expressed as prior knowledge and new remote sensing observations are taken as new knowledge;（ 4） to derive the product of land surface parameter at demanded scales based on error analysis and then update the post trend surface into prior knowledge warehouse. As a whole,c onstruction of geographic trend surface lays the foundation of the methodology,which aims at addressing problems of information insufficiency or key information reservation in scaling. The paper provides a conceptual scaling model,while many technical details need to be fulfilled further in practice.关键词：scale effects;scaling;trend surface;prior knowledge;quantitative remote sensing
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发布时间：2021-06-10 - 摘要：The phase transition of pore water in soil during freeze-thaw process has a great impact on hydrologic cycle,meteorology and soil erosion on both regional and global scale. A useful indicator to evaluate the soil freeze-thaw intensity is the amount of Phase Transition Water Content（ PTWC） in soil pores. However,the coarse resolution of passive microwave data（ about 25 km）has limited availabilities for many applications in environmental monitoring. In this research,we attempted to obtain high resolution PTWC using MODIS and AMSR2 products. We analyzed the ground measured soil moisture and temperature data obtained in T ibet during the winter of 2012 and found that there was a power function relationship between Temperature Index（ TI） and P TWC.Then a downscaling approach combining TI calculated by MODIS products and AMSR2 PTWC was performed to retrieve high resolution PTWC. A satifying result was achieved that the MODIS TI and AMSR2 PTWC also followed the power function trend with a R2 of 0. 8068. The downscaling PTWC had more variation information and added the mission data of AMSR2 PTWC at 25 km. The downscaling PTWC at 1 km was validated with in situ data measured from November 2011 to March 2012 in CTP-SMTMN small scale area. The result showed that the downscaling PTWC was closer to the 1: 1 line and it quite followed the trend of ground data with a RMSE of 0. 0085（ m3/ m3） and MAE of 0. 0059（ m3/ m3） when PTWC was higher than 0. 01（ m3/ m3）. But this method also has some disadvantages. The precision of AMSR2 soil moisture products directly influence the accuracy of the downscaling PTWC. At the same time,we use a power function to do the downscaling approach,the PWTC is i ncreasing very quickly as TI decreasing when TI is near 0. A little change of TI may generate a large change of PTWC. The characteristic of power function may cause errors between retrieved and observed PTWC. In addition,the unmatched scale between the remote sensing pixels and in situ points,the spatial variability by factors other than LST such as soil initial moisture,soil texture,topography and vegetation may also generate errors. In the future research,we can improve the precision of downscaling PTWC by using a high precision soil moisture product,or developing a high accuracy algorithm to calculate PTWC. Introducing the topography,vegetation to the downscaling approach maybe a lso worth to try. In general,the TI-PTWC model in this research has combined the advantage of m icrowave remote sensing and thermal-infrared remote sensing; it has a high precision and can generate PTWC in small scale.关键词：PTWC;Downscale;AMSR2;MODIS;CTP-SMTMN
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发布时间：2021-06-10 - 摘要：Various remote sensing sensors observe the Earth’s surface at different spatial resolutions. Due to the spatial heterogeneity and model’s nonlinearity,there would be some scale difference among different remote sensing surface parameter（ such as leaf area index,LAI） derived from remote sensing images with different resolution. In this paper,the spatial scale effects and transformation methods are studied using both experiment at Xilinhaote steppe region and theoretic models. Firstly,different upscaling methods were presented to simulate the scale effects between fine resolution and coarse resolution. Secondly,Taylor expansion was conducted for both NDVI model and reflectance model for LAI estimation,and the nonlinearity can be well explained by the s econd derivatives. The scaling difference was reduced from 5. 6% to 1. 45% and 0. 78%,respectively,if the contributions of the second derivatives were corrected for LAI models based on NDVI and reflectances of red and NIR bands. Finally,the effects of the nonlinearity and heterogeneity on scaling are quantified. It can be observed:（ 1） the scaling error increases with the vegetation c overage under same spatial heterogeneity;（ 2） the heterogeneity in red band is about 100 times sensitive to scale error than it in near-infrared band for high NDVI conditions;（ 3） for terrestrial vegetation region,the LAI would be underestimated at coarse resolution. The nonlinearity of the exponent LAI model based on NDVI is the primary factor,and the nonlinearity of NDVI variable contributes about 23. 5% scaling difference;（ 4） for wetland region（ mixed by vegetation and water）,the LAI would be overestimated at coarse resolution. The nonlinearity of NDVI variable becomes the dominant factor,and the scaling difference can still be c orrected by the contribution of the second derivates of the LAI model based on reflectances of red and NIR bands. Therefore,we developed a series methods and models to quantify the scale effect of LAI,and the scaling error was consistent with contributions of the second derivates by Taylor expansion,which can also be applied to other surface parameters.关键词：scale effect;upscaling;leaf area index（LAI）;taylor expansion;non-linear;spatial heterogeneity
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发布时间：2021-06-10 - 摘要：Urban thermal environment studies require high spatial and temporal resolutions,while currently available remote s ensors exhibit a compromise between spatial and temporal resolutions. Downscaling land surface temperature（ LST） data with a low spatial resolution but high temporal resolution is a method to derive LST product at application-demanded spatial and temporal resolutions and has become a critical issue in thermal remote sensing applications. This study aims at downscaling LST from coarser spatial resolution（ 960 m） to finer（ 120 m） resolution by constructing a trend surface of spectral indices over highly heterogeneous urban areas,to lay a foundation for potential downscaling of MODIS LST to TM spatial resolutions. To describe the complex composition of built-up areas and vegetated surface in urban and surrounding areas,normalized difference built-up index（ NDBI） and normalized difference vegetation index（ NDVI） are combined to construct a trend surface to downscale LST from 960 m to 120 m.Urban areas in Beijing is taken as a study case. The key is to construct a trend surface of spectral indices to fit the LST,that is,to derive a trend surface transfer function at 960 m,and then to use the transfer function established at 960 m to estimate LST at 120 m using spectral index trend surface at 120 m. To validate the applicability and stability of trend surface transfer function,the fitting degrees of spectral indices to LST are analyzed from the central urban areas to the sub-urban in Beijing and at varying scales from 120 m,240 m,480 m to 960 m; the scale effects of trend surface transfer function is also analyzed. The linear functions of transferring NDVI,NDBI and combined NDVI + NDBI to LST are established respectively to downscale LST from 960 m to 1 20 m.The results show that:（ 1） the LST in Beijing is the highest in urban central areas surrounded by the fifth ring road and is gradually decreasing from areas within fifth ring road to areas outside the sixth ring road; the built-up and bare soil areas have the highest average temperature values,while vegetation and water bodies have low average temperature values;（ 2） NDBI fitted the LST the best over the densely distributed built-up urban areas,while NDVI is more suitable to fit LST over naturally covered areas outside the sixth ring road; the combined use of NDVI + NDBI can promote the fitting degrees to LST,depending on the composition of land cover types;（ 3） the correlation of spectral indices and LST applies on four varying scales from 120 m to 960 m. The errors induced by scale effects of transfer function are much lower than the errors brought by the models themselves;（ 4） the accuracy of downscaled LST using combined NDVI + NDBI model is promoted,compared to those using a single spectral index. Yet the degree of accuracy enhancement depends on the composition of land cover types;（ 5） downscaling errors are related to aggregation errors.Although aggregation errors are higher than downscaling errors,they remain an important source of downscaling errors. The methods in this paper is also applicable to downscaling of MODIS LST data,while many factors such as temporal difference c orrection and geometric correction should be considered further.关键词：land surface temperature;down-scaling;Normalized Difference Built-up Index（NDBI）;Normalized Difference Vegetation Index（NDVI）;trend surface
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发布时间：2021-06-10 - 摘要：Canopy characteristic scale is a basic concept of quantitative remote sensing of vegetation,but also a very important feature,expression is of great significance for his physical definition and quantitative. The canopy characteristic scale physical c onnotation understanding and modeling mathematical expression,is the basic of research object linear and non-linear mixed,and it is the premise of optimal scale of observation objects. From the ray radiation transport point of view,there is a characteristic scale nonlinear mixed into the linear mixed transition,incident radiation between the transition characteristic scales of the object is independent of the optical properties,which can better describe the canopy group,choose the appropriate scale can get twice the result with half the effort to make remote sensing data.Base on the physical definition of optical radiation transmission proposed canopy characteristic scale,we established a mathematical calculating model for the canopy characteristic scale,and introducted the inverted geostatistics index model,put forward the calculation method of canopy characteristic scale based on analysis of local variance. Using the forest area of high resolution i mage,the canopy characteristic scale model proposed in this paper provide a quantitative validation. We chose 19 areas of higher density and vigorous growth forest as a study area,all the study areas with the same planting and regular distributed,and approximately at the same stage of the operation. Data come from the Google Earth aerial photography data,with a resolution of 0. 3 m,the data from two regions in Macon and Locke.The canopy characteristic scale model for analysis of data from the two regions,There is a certain correlation between the canopy characteristic scale model calculated and forest spacing measured,the linear correlation coefficient of 0. 95,that showed spacing and vegetation canopy characteristic scale of the two regions is the presence of certain stable relationship,canopy characteristic scale calculation of canopy characteristic scale model was about 1. 25 times the spacing.Canopy features scale models thesis,which can be in remote sensing images of forest vegetation growing season to choose their appropriate characteristic scale,canopy characteristic scale model is reasonable and feasible. Firstly,mixed pixel above the canopy characteristic scale belong to linear mixed,for its various transformations,you can use linear mixed pixel method to solve various scale difference or scale effect problems,Secondly,canopy characteristic scale is a basic object scale,when vegetation p arameter population sampling（ such as leaf area index）,canopy spectrum measurement,the sampling plots close to or greater than the canopy characteristic scale,can reflect the vegetation canopy component information; When the resolution is close to or below the canopy characteristic scale,ground surrounding pixel block or cross radiation effects to a minimum,arrive downward radiation level surface solar radiation spectrum and the corresponding pixel under the surface of the ground,ground reflectance can be calculated accurately.关键词：canopy characteristic scale;local variance;exponential model;mixed pixel;quantitative remote sensing
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发布时间：2021-06-10 - 摘要：The quality of remote sensing Leaf Area Index（ LAI） products and the uncertainties of the products are evaluated with ground measurements. The spatial scale mismatch problem of these two data should be solved through upscaling before c omparing these two dataset. To evaluate remote sensing LAI products using ground measurements,the spatial scale mismatch problem of these two datasets should be solved first. A new approach based on Taylor Series Expansion Model（ TSM） was p roposed in this paper. It combines the information of high-resolution images,NDVI-LAI empirical model and the LAI ground measurements to generate the upscaled LAI at the coarse-resolution scale. This approach not only can upscale ground measured LAI to coarse-resolution,it also can provide upscaling accuracy for each pixel. The LAI measurements collected in 2008 in the Heihe experimental research region was used to test this method. The possible error associated with this method is from two sources. One is neglection of the third- and higher-order TSM terms,which can be estimated using Taylor series remainder. Another is the uncertainty of the empirical model. The total error of the upscaling method is the sum of these two errors. The data used in this study were collected during the Watershed Allied Telemetry Experimental Research（ WATER） project. The ground measurements were collected in the Yinke Oasis and the Huazhaizi Desert experimental area using LAI-2000,TRAC,fisheye camera,and by manual measurement. The corn LAI dataset was chosen for analysis. Airborne CCD and ASTER data were used to help upscaling to ground measurments. Upscaled LAI ground measurements were compared with NDVI-LAI empirical model calculated LAI and MODIS,GLASS LAI product at 1km scale. The accuracy of the upscaling process was used as reference to select the upscaled LAI. Empirical model c alculated LAI is usually considered as more reliable validation data,but it’s not directly associated with ground measured LAI,and can’t provide accuracy of each pixel. Comparison with empirical model calculated LAI shows that this approach can provide reliable result. Also the accuracy of this approach is a good indicator for selecting validation data. By using the upscaled LAI with a cceptable accuracy only,comparison with MODIS and GLASS LAI products show that both products are lower than ground measured LAI at this region.In comparison with other validation method,this method can improve the representativeness of ground measurements by combining more information at the sub-pixel scale and considering the heterogeneity of the land surface. Consequently,this method is suitable for validation studies in which the field-measured data are derived from non-u niform surfaces at coarse-resolution pixel scales. It’s a new approach to upscale ground measurements for validation of coarse-r esolution products. Mostly important is that the upscaling accuary can be estimated for each pixel,which can provide a reference of how to select high quality ground measurements for validation. Though comparison with MODIS and GLASS LAI products in Heihe region shows both products are underestimating LAI,but this conclusion is not suitable at global scale.
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发布时间：2021-06-10 - 摘要：The Ice,Cloud,and land Elevation Satellite-2（ ICESat-2） will use a multiple-beam photon-counting Li DAR system to observe the earth surface with profile model at 532 nm. Given that the photon point cloud with low point density captured at a 490 km high altitude is highly affected by noise,especially those from solar background,traditional filtering algorithms for point cloud are not appropriate to the profile photon returns.We propose an automatic filtering algorithm for the photon profile data acquired by the Multiple Altimeter Beam Experimental Li DAR（ MABEL） system,a high-altitude airborne profile-laser altimeter designed as a simulator for ICESat-2. In addition,the basic principle of the multiple-beam photon-counting system,the property of the photon point cloud,and the tree height retrieval method are discussed.The data processing of MABEL points in this paper mainly consists of three steps.（ 1） Denoising was implemented in the acquired MABEL data. The frequency histogram for the accumulated distance between the given point and its k-nearest neighbors was assumed as Gaussian-like distribution,whereas the point with its accumulated distance far from the mean distance was recognized as noise.（ 2） The designed filtering algorithm was applied on the denoised MABEL data. The points were divided into sections along the profile; the lowest point in each section was selected as initial ground returns. Ground points in a window that covers several sections were adopted for modeling the second order local terrain. Least square fitting method was used to determine the local curve parameters. The points in each section were classified into ground points and no-ground points using the adaptive threshold.（ 3） The labeled ground points were used to produce line-like Digital Elevation Model（ DEM） by an interpolation method. The mean vegetation height in the study area can be estimated by comparing the local maximal laser points with the line-like DEM.The point clouds acquired by the MABEL 532 nm channel at Sierras-Forest in September 2012 were tested with the filtering algorithm. Approximately 2001 points were acquired in the experiment area with 2310 m long. After applying the proposed filtering algorithm,760 points were labeled as ground,1159 points were labeled as vegetation,and the remaining 82 points were labeled as noise. Validation results showed the total classification accuracy of the proposed algorithm was 97. 6% compared with the manual method. In addition,the line-like DEM and the mean tree height（ 34. 2 m） in the study area were estimated; error analysis was also performed.This paper introduced the basic principles of multiple-beam photon-counting Li DAR of ICESat-2,a novel point cloud filtering algorithm for MABEL data to separate ground and vegetation points. Both the DEM and the mean tree height were estimated from the filtered data. The following conclusions were obtained:（ 1） The experiment shows that the filtering algorithm is efficient and self-adapting for separating the ground and vegetation MABEL point.（ 2） The noise contained in the MABEL data lead to failed labeling,and directly increases the errors of the estimated DEM and tree height.（ 3） Given the noise level of the ICESat-2 data may be higher than the MABEL data,further studies are needed on both data denosing and ground points detection.关键词：ICESat-2;MABEL;micro pulse photon-counting;profile point cloud;de-noising and filtering;tree height retrieval
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发布时间：2021-06-10 - 摘要：Redundant point clouds are inevitable in the overlapping area of adjacent airborne Li DAR strips. Data redundancy c auses problems in terms of data density and accuracy consistency compared with other regions. Reduction of airborne Li DAR data isan important step in Li DAR data pre-processing to distribute the laser points more evenly and eliminate accuracy inconsistency.This paper aims to reduce point cloud without auxiliary data. An algorithm based on time and texture information extracted from point cloud is proposed.（ 1） Point clouds are partitioned into different strips using cluster analysis method according to their GPS time.（ 2） A texture graph is generated by analyzing the Li DAR scanning mode and projecting point coordinates to scanning detection,from which the key points are extracted and connected into strip edge lines.（ 3） When holes caused by the occlusion of high objects appear: Ground and non-ground points are separated automatically by adaptive TIN filtering,followed by classifying high object points based on region-growing method,and finally detecting the edges of the high objects by Alpha-Shapes algorithm. A fter combining with other the information contained in point clouds,the holes occluded by high objects can be detected.（ 4） Remove of the redundant point clouds with low accuracy in the overlapping area by comparing the distances of the points to their strip edge lines. Thus,the closer distance points from their strip edge lines,the lower accuracy these points have. The redundant and low accuracy point clouds in the overlapping area were efficiently removed and the hole areas were reserved. Meanwhile,the accuracy of the DEM generated from the simplified data slightly improved. Experiments results showed the effectiveness of our method,which outperformed the conventional data reduction approaches in terms of the fact that flight trajectory information is necessary in those methods.关键词：airborne Li DAR;time and texture information;overlapping strip area;shadow hole;data simplification
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发布时间：2021-06-10 - 摘要：An objective in the field of EM-based wave decomposition when dealing with full-wave Li DAR data is to improve the processing efficiency. We proposed a method that parallels the EM algorithm on GPGPU.（ 1） Considering CUDA memory hierarchy that can access shared memory and texture memory with high speed,we designed an overall parallel framework.（ 2） A median can be obtained from the histogram of the waveform sampling values that are stored in bytes to reduce the complexity of noise threshold calculation.（ 3） A parallel strategy for summation was applied to improve the accumulation efficiency during the EM iterative process. Results indicated that the GPU speed ratio using the proposed method can reach 8 when meet the following conditions:（ 1） Proper parallel parameter setting;（ 2） EM iteration number is larger than 16;（ 3） Data size is larger than 64 MB.Therefore,the processing efficiency of full-wave decomposition can be significantly improved under the GPU parallel computing framework.关键词：full waveform decomposition;Li DAR;GPGPU;parallel
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发布时间：2021-06-10 - 摘要：High-voltage power transmission line is an important infrastructure of a country,the breakdown of power facilities may bring huge damage to the daily lives of the people and the economy of the country. Thus,management and monitoring of power f acilities is important. Traditional engineering measures have the drawbacks of high workload,high risk,and low efficiency. Airborne Li DAR can overcome these drawbacks,and cannot be easily affected by environment,making Li DAR an important d evelopment trend for power line measurement. At present,airborne Li DAR is mainly used for the reconstruction of urban areas and natural features; extraction and modeling of power facilities is still in its infancy. The main problems are too much human i ntervention,low accuracy,and lack of continuity. Therefore,this paper proposes a new method based on the analysis of the characteristics of power facilities and extraction algorithm. First,the optimal catenary fitting geometry model of the powerline was o btained based on the powerline characteristics. The nonlinear catenary equation was simplified to linear polynomial form based on the principle of Lagrange polynomial to improve the operation efficiency. Second,the projection direction of the powerline in the XOY plane was determined based on a statistic method. Afterward,the vertical plane that contains the starting and end points and are perpendicular to the power line were also determined based on the characteristic of the end laser point. The distance of all points to the plane as X-axis parameters was calculated and the points corresponding to the Z coordinate values as Y-axis parameters were obtained to determine the optimal plane coordinate system. Finally,the quadratic-polynomial-limited least square method was used to fit the powerlines,obtain the optimal parameters,and generate the final power line model. Experiments on real data showed that the proposed method improved the facility and veracity of powerline fitting. The precision reached ± 1. 740 cm both in the vertical and horizontal planes. The proposed method can quickly fit the powerline and reach a high accuracy,which are significant in the study of 3D reconstruction by Li DAR point cloud.关键词：Li DAR;point cloud data;power line;three-dimensional
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发布时间：2021-06-10 - 摘要：Vehicle-mounted mobile system based on panoramic camera can obtain panoramic images with exact position and p osture information. Based on these data,we propose a method to generate panoramic epipolar images. First,we describe the process of constructing geometric constraints between two panoramic images in the spherical panorama model. Then the two panoramic epipolar images are matched using the SIFT algorithm. Finally,according to the principle that the center of photography,namely image point and object point,are collinear,the collinearity equation of the panoramic images are deduced and the three-d imensional coordinates are calculated by the principle of the forward intersection.The following steps are used to generate panoramic epipolar images. First,the baseline direction is determined according to the position and attitude of two panoramas. The Z axis of the panorama is then turned to the baseline direction by two rotations. F inally,the panorama is projected to the plane image according to the new coordinate.The two panoramic epipolar images are matched using the SIFT algorithm to remove certain error matching points by panoramic epipolar geometry. This paper does not apply the conventional collinearity equations,but derives the relations between image point and object point in spherical model,and then in a unified coordinate system,derives the relations between corresponding i mage points in panoramic images and object point. Finally,the three-dimensional coordinates are calculated by the principle of forwardintersection. The coordinates（ three values: X Y Z） for a pair of corresponding points can be calculated by four equations（ one image corresponds to two equations）.Three experiments were designed to verify the validity of the methods. In the first experiment,generating panoramic epipolar images,the column numbers of the homonymous points in two panoramic epipolar images are close,indicating that the epipolar images are correct. In the SIFT matching experiment,no obvious error matching points were observed for the panoramic epipolar image matching. In the final experiment,the three-dimensional coordinates was calculated using the panoramic intersection. The object space points calculated with the points in the point cloud were then compared to obtain the coordinate accuracy of about 1 0 cm to 20 cm.Experimental results showed that the proposed method to generate panoramicepipolar images is effective,achieves the correct panoramic collinearity condition equations,can reduce the difficulty in panoramic image matching,improves the quantity and p recision of the matching,and can be used to implement measurement based on panoramic images.关键词：panoramic image;epipolarline;image match;intersection;mobile measurement system
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发布时间：2021-06-10 - 摘要：Currently,automated reconstruction of buildings from data acquired by airborne Li DAR has been an important research topic in photogrammetry,especially for the precise reconstruction of complex buildings. Given the various and complex structures of buildings and the discrete distribution of laser data,automatically reconstructing complex buildings using airborne Li DAR data is complex. Certain methods are only suitable for buildings with simple structure,whereas some methods reconstruct the building models with the aid of other data source,such as remote sensing images and ground plans. Hence,this study proposed an automated approach for complex shape building reconstruction based on key point detection that only makes use of airborne Li DAR point cloud to solve the problems on the automated reconstruction of complex buildings. The point clouds of different roof planes are extracted automatically by combining RANSAC segmentation and space segmentation. For each plane,the exact contour is picked up using the Alpha Shape algorithm. Public intersection line features are determined by topological relation of these planes,which will help correcting the initial key points. Finally,the precise building model is obtained. Detection of the key points is important for our method,which contains extraction of initial key points,judgment of topological relation of planes,correction of key points,and model reconstruction. During judgment of topological relation of planes,three pubic intersection line features should be determined,namely,public line segment,public radial,and public line. The final key points are then calculated correctly according to these restraints. Two different types of buildings and one urban area were chosen to verify our method. Building 1 is composed of many horizontal roofs with different height levels,without intersection among those roof planes; whereas Building 2 consists of some gable roofs with relative more complex relationship. The experimental results showed that RANSAC and space combined segmentation is fantastic,the initial key points present the sketchy structure of the building roof,the vectorized roof models show the public intersection line features reasonably,and the final reconstructed building models fit the remote sensing images well. To further validate the effectiveness of our approach,the same two buildings were processed using Terra Solid and the models were compared with our results. The comparison proved that the accuracy of our approach is close to that Terra Solid. The selected urban area contains flat roof buildings,gable roof buildings,L shape buildings,hipped buildings,and complex structure buildings c ombined with the above typical shapes. Successful reconstruction of this area proves that our approach is suitable to urban area point cloud with complex shape buildings and is effective and practicable. Our approach realized the automated reconstruction of complex shape building models by point cloud segmentation,contour extraction,and key point detection. Experimental results of different types of buildings and one urban area showed that:（ 1） Our method can reconstruct complex-shaped buildings effectively;（ 2） Compared with TerraSolid the reconstructed results,the accuracy of our method is close to that of Terra Solid and is valuable;（ 3） The method is restricted by point cloud segmentation. If the threshold in the segmentation is rigorous,some roof plane point clouds would be lost,which would have side effect on the final reconstruction results.关键词：building reconstruction;airborne Li DAR;point cloud segmentation;contour extraction;key points detection
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发布时间：2021-06-10 - 摘要：This paper presents a new algorithm for SAR sea ice image segmentation by combining the speckle reduction region growing（ SRRG） model with region-level MRF models. Region-level MRF-based algorithms are widely used in SAR sea ice image segmentation. The over-segmentation degree of the initial areas and the localization of the target edges significantly affect the c omputational complexity and segmentation accuracy of region-level MRF-based algorithms. Serious over-segmentation leads to increased computational complexity,whereas accurate target edge position is beneficial to obtaining precise segmentation results. Given that existing region-level MRF-based segmentation algorithms are inadequate for determining the effects of speckle noise and the relationships between regions,the segmentation process usually needs more time,and the probability of false segmentation increases. In other words,the segmentation accuracy is usually low. Hence,this paper proposes a new segmentation algorithm based on region-level MRF models combined with a speckle reduction region growing model（ SRRG-MRF）. This proposed SRRG-MRF can effectively reduce the interference of the speckle noise and significantly improve segmentation accuracy by fully considering the similarity between adjacent regions. The SRRG model includes two parts: construction of an image speckle reduction r egional representation and region growing based on the gray similarity of adjacent regions. For the former,speckle noise is initially suppressed using the proposed Speckle Reduction Bilateral Filter（ SRBF） algorithm. The region adjacency graph is then built to obtain regional representation of the image based on watershed transform. The SRBF algorithm can thus effectively inhibit the w atershed over-segmentation and achieve accurate positioning of the target edges. For the latter,the gray similarity of adjacent r egions can initially describe the local characteristics of the image more accurately than the edge strength. The gray similarity p enalty function of the adjacent areas is then introduced into the region MRF model based on the Gamma distribution. The region merger guideline is subsequently defined for region growing by calculating the energy difference between adjacent regions. Combining the SRRG region model with the MRF model can significantly reduce the optimization search space,prevent MRF segmentation optimization into localminimum,and reduce false segmentation to obtain accurate results. The proposed segmentation algorithm was evaluated using several synthetic SAR sea ice images corrupted with various levels of speckle noise and the real SAR sea ice images obtained by RADARSAT-2 and SIR-C,respectively. The overall segmentation accuracy and κ coefficient were used for algorithm evaluation. First,the experiment compared the number of watershed segmentation regions without filter processing and five kinds of filter processing,such as traditional Bilateral Filtering,Enhanced Lee,SRAD,and SRBF algorithm. Results showed that the SRBF algorithm is more effective in inhibiting over-segmentation and can obtain the accurate position of the target edges. The experiment then compared the proposed algorithm with the existing region-level MRF-based algorithms,namely,RMRF,IRGS,and EPR-MRF. The new segmentation algorithm substantially improved the segmentation accuracy and κ coefficient compared with the existing regionlevel MRF-based algorithms. The testing results demonstrated that the proposed algorithm is an effective and feasible method for SAR sea ice image segmentation. On one hand,SRRG-MRF can effectively reduce the interference of the speckle noise to inhibit watershed over-segmentation and achieve accurate positioning of the target edges. On other hand,SRRG-MRF can reduce the optimization search space,prevent MRF segmentation optimization into local minimum,and r educe false segmentation to obtain accurate results.关键词：SAR;sea ice segmentation;speckle reduction;bilateral filtering;region growing;MRF
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发布时间：2021-06-10 - 摘要：The classical methods of two dimensional remote change detection are no longer working effectively in three dimensional change scenes,such as geographical disaster areas. Vegetation information extraction also generally requires near-infrared bands.Visible light images from 2010 and 2011 in Yingxiu are selected and vegetation and three dimensional terrain change detection methods are presented under conditions without near-infrared bands. The Digital Elevation Model（ DEM） and Digital Ortho Map（ DOM） in two periods are generated by DPGrid. Then,vegetation changes in DOM are detected by the CIE Lab and Otsu a lgorithms. This study proposes an adaptive threshold method of three dimensional change detection based on the theory of probability and statistics. After censored samples are selected,the threshold range of elevation change is obtained by the 3σ rule of Gaussian distribution. High risk areas of geological disasters are extracted under the condition of high probability confidence r egions. Finally,the corresponding earthwork change quantities are estimated by discretized integral. Vegetation changes in two p eriods are successfully detected,and change areas are located on the edge of the canyon in floodplain. This study determines the elevation change threshold range of three dimensional change detection by calculation. The threshold values of elevation decrease and increase are 2. 73 m and 2. 43 m,respectively. Landslide high risk areas and two debris flow accumulation areas located along Minjiang River are successfully detected. The earthwork quantitative change of 10 landslide high risk areas and two debris flow a ccumulation areas are estimated. Results show that the proposed method is effective,feasible,and practical. This study not only elevates conventional two dimensional change detection into three dimensional space but also quantitatively estimates three dimensional terrain changes. Therefore,the proposed method can be applied to monitor dynamic remote sensing and evaluate geological disasters. Even without near-infrared band,the proposed method can still successfully detect vegetation changes. In this study,s tatistical methods are used to determine the elevation change threshold. In allusion to landslide disaster,elevation-significant r eduction risk areas are extracted. The distribution of risk areas is consistent with other research results. With discretized integral calculation,this study explores the earthwork quantity estimation method based on DEM. This method breaks the assumption that the surface is continuous and gradual,and can quickly estimate earthwork quantity by DEM. This method also incurs some errors caused by limited data source. The quantitative analysis for the uncertainty of the error is the main research target in the future.The two DEMs are generated separately in two periods; thus,they have their own errors in three dimensional change detection. Errors may also be amplified in error propagation. In future studies,the geographic information system data from previous periods will be used as control information to generate a DEM for a new period. Doing so would reduce relative errors. A domain-specific model will be also used to analyze and evaluate geological disasters.关键词：geological disasters;three-dimensional change detection;DEM;stereopairs;adaptive threshold;earthwork quantity;discretized integral
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发布时间：2021-06-10 - 摘要：The 34 thAsian Conference on Remote Sensing（ ACRS） was held during October 20— 24,2013,in Bali,Indonesia.The theme of ACRS was"Bridging Sustainable Asia". Based on the conference proceedings,we analyzed and summarized the 970 a bstracts from 52 countries and regions,then conducted a thorough review of the current situation of technological development in terms of current progress trends and research directions for remote sensor,data processing,and remote sensing applications.A thorough literature review of the conference proceedings was carried out to identify the current progress and future research directions of remote sensing technologies and applications. Based on the contents of the conference abstracts,papers presented at the conference were grouped into three categories: sensor technology,data processing,and application. Then we provided an overview of the current status and future research directions in the three areas.Results indicated that the conference articles were extremely rich,including dynamic and basic academic disciplines representing cutting-edge remote sensing science and technology. This review also found that Asian countries illustrated significant achievements in remote sensing technology development and applications. China（ including Taiwan and Hong Kong） contributed the most abstracts to this conference,indicating that China is now an academic powerhouse in Asian remote sensing research.A total of 702 conference abstracts elaborated on the theme of remote sensing applications,accounting for 72% of the total a mount of papers. These applications mainly concentrated in the research of marine,agriculture,disaster,cities,forests,watersheds,and land use monitoring. A total of 226 abstracts elaborated on the theme of remote sensing technology,accounting for 23%of the total amount of conference papers that focused on data processing,LIDAR,satellite launch,UAV,and software and hardware d evelopment. This review also found some hot topics,such as Small Satellites and Big Data. However,these topics were not d iscussed much at this conference,suggesting that they may need more attention at future events.关键词：ACRS 2013;senser;data processing;applications;literature analysis
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发布时间：2021-06-10 - 摘要：<正>高分二号卫星是中国自主研制的首颗空间分辨优于1m的民用光学遥感卫星,观测幅宽达到45 km,在亚米级分辨率国际卫星中幅宽达到先进水平,同时具备快速机动侧摆能力和较高的定位精度,有效地提升了卫星综合观测效能。封面图片为2014年9月29日国家国防科技工业局发布的上海市区0.8m全色与3.2 m多光谱融合影像,侧摆角为15°,纹理清晰、层次分明、信息丰富。高分二号卫星在土地利用动态监测,矿产资源调查、城市精细化管理、交通路网规划、林业资源调查、防灾减灾、环境保护与监测以及农作物估产等众多领域具有广泛的应用潜力。
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发布时间：2021-06-10
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