摘要:Thermal infrared remote sensing mainly provides information on the radiation of the land surface,whereas passive m icrowave remote sensing can provide information on the radiation of vegetation and soil vertical structure. Therefore,the new a pproach,which combines the advantages of thermal infrared and passive microwave remote sensing,aims to improve inversion precision through the synergy inversion of vegetation and soil component temperature. In this paper,a comparative analysis is c omprehensively performed between the Emissivity and Scattering by Arbitrarily Inclined Leaves Model( Easail) and the Microwave Emission and SCAttering Model( MESCAM). A unified scene of homogeneous crops is also constructed,wherein the parameters of the two models are divided into two categories: direct and indirect. The scene construction and Leaf Area Distribution( LAD)function of the MESCAM model is modified. The microwave radiation brightness temperature is then computed by introducing component temperature variables into the MESCAM model. Therefore,a joint simulation model,United Easail Modified MESCAM model( UEasmmes),is constructed. The UEasmmes model is adopted to simulate the Directional Brightness Temperature( DBT)over homogeneous corn. The sensibility differences of the component temperature,component emissivity,leaf area index,and LAD toward the thermal infrared and passive microwave DBT are analyzed in depth. The simulation results reveal the feasibility of retrieving component temperature by combining thermal infrared with microwave remote sensing. However,f urther research is needed to determine how to overcome the significant change in brightness temperature induced by canopy effective emissivity in the microwave domain owing to the change in component emissivity,LAI,or LAD and the interconvert between the thermal infrared surface temperature and the microwave equivalent temperature.
摘要:Leaf Area Index( LAI) is an important land surface parameter in global carbon cycle studies. Over the past decades,v arious of algorithms for retrieving LAI from satellite data have been developed. However,the continuity of LAI time series is still need to be improved. In this study,we present an algorithm for retrieving LAI dynamics based on the framework of Dual ensemble Kalman Filter( Dual EnKF),which updates the estimation of LAI and its sensitive parameters in a dynamic model,and gets optimal results simultaneously. The Dual EnKF algorithm can get better estimation results of the dynamic model,and assimilates remote sensing data into the dynamic model to get optimal estimation results of LAI. Three sites with the land cover of cropland,grassland,and forest are employed to validate this algorithm. The validation results show that the LAI temporal profile estimated by D ual EnKF method is very continuous with less fluctuations and abrupt change points; the estimation results of temporal dynamic model is improved,even when high quality remote sensing data is not available. LAI time series is in good agreement with the r ealistic LAI climatology.
摘要:Atmospheric parameters are crucial to the atmospheric correction of remote sensing images. However,the universality of most atmospheric parameters Look-Up Table( LUT) at present is not strong enough,e. g.,the LUT data storage space is huge and the specific implementation steps are not clear. We build a universal multidimensional atmospheric parameters LUT which not only has the 1 nm spectral resolution but also is suitable for multiple remote sensing sensors. The LUT is stored as binary data and adopted the multidimensional Lagrange and multidimensional inverse distance interpolation method to input parameters. The results of the study show that with binary data stored look-up table can both reduce disk storage space to the hilt and achieve fast look-up table data random read. Relative to the multidimensional inverse distance interpolation method,multidimensional Lagrange interpolation method is faster,of higher precision with error about 1. 0%.
摘要:Airborne LiDAR is now becoming a rapid method to acquire three-dimensional coordinates of the ground objects automatically,which has great potential applications on power lines inspection. According to the demand of the intelligent power lines inspection,a power lines extraction method based on spatial domain segmentation was proposed in this paper. First,the method a dopted the elevation histogram statistical method to remove ground objects. Second,pylons along the corridor were identified based on the difference of the point cloud density. Third,single power lines was isolated from each other based on the distance threshold between two neighborhood lines and height threshold between two neighborhood level of lines using a spatial domain segmentation algorithm. Finally,a simplified polynomial model was applied to model the geometry of each power line in three-d imensional space. Experimental results showed that the method can be applied to extract power lines with multiple spans simultaneously,and demonstrated its potential advantages in power-lines inspection application.
摘要:This paper proposes an adaptive Markov Random Field( aMRF) model for the dense matching of deep space exploration images,which usually lack textures. Compared with traditional MRFs,the approach improves dense matching accuracy by a combination of adaptive disparity range predictions,adaptive matching windows,and adaptive weight coefficients. Furthermore,aMRF reduces the disparity search range while accurately preserving disparity discontinuities. Real rover images from the Mars E xploration Rover mission and Chang’E lunar orbiter images experiments demonstrate the effectiveness of the proposed method.
关键词:deep space exploration;dense match;markov random field;adaptive
摘要:In High Spatial Resolution Remote Sensing( HSRRS) images,targets in the same class have different shapes. The d escription at one scale or one template is inadequate to describe target shapes from the same class. In this study,a multiscale shape model based on wavelet transform and Fourier descriptors is constructed. A new object-oriented method for target recognition in HSRRS images is also developed. The model uses wavelet approximation coefficients with successively decreasing scale to represent the target shape from top to bottom approximately. Approximate shapes are described quantitatively using Fourier d escriptors.The final recognition results are obtained using the semantic rule to synthesize recognition results at multiple scales. This method can reduce the effect arising from the broken objects segmented at a small scale and the underidentification of small objects at a large scale. Aircrafts and buildings in HSRRS images are identified,and the comparison results show that the method proposed in this paper has higher identification accuracy.
摘要:Craters can be defined in terms of terrain attributes calculated from the Chang’E-1( CE-1) Digital Elevation Model( DEM),and detected by using Hough Transform. We applied our method to the surrounding area of Klavius crater,and compared the results to the existing catalog of manually identified craters. The factors for our algorithm are: detection percentage( D) =71%,branching factor( B) = 0. 30 and quality percentage( Q) = 58%. If we excluded the craters that our method is not capable of d etecting then D = 90%,B = 0. 30,and Q = 71%. The performance of proposed DEM-based algorithm is better than imagebased algorithm,especially the small number of false detections. The experiments demonstrate that the algorithm was able to detect the lunar craters from CE-1 DEM.
摘要:Digital Elevation Models( DEMs) for Antarctica are critical datasets for general circulation models,change analysis of ice sheet dynamics and logistical planning of field expeditions. To date,there are five different DEMs covering all of Antarctica,all of which were derived from satellite radar or laser altimetry data in combination with ground data. Since the margins of ice sheets are highly dynamic in space and time,DEMs of Antarctica should be updated frequently as new data become available. We used Radar Altimeter( RA-2) data from Envisat and laser altimeter( GLAS) data from ICESat to create an up-to-date DEM of Antarctica with high accuracy and precision of elevation measurement. In this paper,these two different sources of satellite altimeter data from 2003 to 2009 were integrated to generate a DEM for the entire continent of Antarctica. We applied five different quality judgment rules to filter unreliable ICESat / GLAS data; as a result,8. 36% of the data were filtered out. A relative correction method based on speckle geometry intersection was used to correct Envisat RA-2 elevations to ICESat / GLAS. The data were interpolated to a regular 1000 m polar stereographic grid using ordinary Kriging after semi-variance analysis. The accuracy of the final DEM was a ssessed through a comparison with two airborne LiDAR datasets,a field GPS strip from China’s Zhongshan Station to Dome A and the most recently published DEM. The comparison result shows that the error of the new DEM is from 3. 21 m to 27. 84 m,and the distribution of errors depends on the surface slope. The new DEM shows an obvious improvement on steep slopes,including the quickly changing ice sheet margin areas.
摘要:Non-stationary nature of SAR images is a major obstacle to the automatic interpretation of SAR sea ice image. Incidence angle effect is one of the main factors leading to instability in the sea ice image features. Based on Radarsat-1 ScanSAR mode data,this paper proposes a new segmentation algorithm which integrates incidence angle effect correction step. Considering both the speckle noise and incidence angle effect,the regional clustering,incidence angle effect correction and region merging will be combined through the pathway starting from the pixel,region and to the large-scale area. The efficiency of the proposed method has been demonstrated on the segmentation of Baffin Bay SAR sea ice image and Gulf of Bothnia SAR sea ice image,suggesting that the segmentation accuracy has been substantially improved in contrast to area-based MRF algorithm.
摘要:The object-oriented change vector analysis method,which is excessively dependent on the mean value of each object but failed to use gray distribution information,is deficient in change detection using high-resolution remote sensing images. A new method introducing similarity measurement of object histogram is proposed in this study. First,the similarity measurement of o bjects between different periods is built up by G statistic. Second,the Expectation Maximization( EM) algorithm is used to calculate the related parameters according to the assumption that all similarity measurement values of objects fit a Gaussian Mixture D istribution model. Finally,the Bayesian rule with minimum error rate is applied to get the change / no change results. Experimental results show that the method can get results with higher accuracy in change detection,especially for high-resolution remote sensing images.
摘要:The spectral reflectance data of Moderate-Resolution Imaging Spectroradiometer( MODIS) from 2001 to 2010 was clustered in spatial and temporal dimension and we analyzed the spatial distribution and temporal changes of land-surface cover color of China. The conclusions are:( 1) Land surface colors are composed of green of vegetation,brown of bare soil,yellow of mixture of soil and vegetation,blue of water and white of snow and ice. Brown color distributed in north-west China is the dominant color in all seasons; Green color of low Normalized DifferenceVegetation Index( NDVI) is the dominant color in spring,a utumn and winter; Green color of high NDVI is the dominant color of summer,distributed in the middle and lower reaches of Yangtze River,South China,south-west and north-east China.( 2) Color in the agricultural regions of Huang-Huai-Hai plain a ppeared in a brown-green-brown-green-brown pattern during the year,that are in agreement with the pattern of crop cultivation in a phenological calendar of first growth-fallow-the second growth-harvest. The unique spectral cluster characteristics of the paddy fields in the middle and lower reaches of Yangtze River and South China imply a mixture of vegetation and water in the sow and growth period.From south to north,crops are sown later and reaped earlier,presenting a trend of decreasing growth period.( 3) The dominant color often transforms from green to yellow in the southern part of Gansu,southern part of Shanxi,northern part of S ichuan,northern part of Shanxi and the northern part of Hebei. Those regions with the most frequent color changes are also the regions with significant greenness change and uncertainty in land use and land cover.
摘要:This paper proposes a self-developed multi-channel ground-based microwave radiometer that uses a brightness temperature and data inversion method. We compared the radiometer data and the radiosonde observations,and then analyzed the radiometer observations in terms of brightness temperature,inversion temperature,and water vapor profile accuracy. Results show that the microwave radiometer has a small observation error of brightness temperature and the neural network inversion profiles of atmospheric temperature,water vapor,and other parameters are accurate and reliable. The proposed radiometer has practical applications.
摘要:Existing endmember extraction algorithms are mainly based on the convex simplex hypothesis. However,the cover types in certain endmembers are not single,which will affect the unmixing accuracy of mixed pixels when performing abundance inversion. In this paper,we propose to determine the nature of the hyperspectral pixel based on the high-resolution remote sensing image. First,a spectral relatively homogeneous vector diagram of blocks is superimposed on the hyperspectral image after the highresolution image segmentation. Second,spatial relations analysis is performed to find the hyperspectral pixels that are within the blocks,which is called a quasi-endmember. Finally,endmember extraction is performed to find endmembers from the quasi-endmember set. The experimental results demonstrate that our approach can reduce the root mean square error of the extraction results by about 20%.
摘要:The HY-2,which was launched on August 16,2011,is the first microwave marine remote sensing satellite developed by China. In this paper,the data of Significant Wave Height( SWH) measured by satellite altimeter HY-2 were compared with the data observed by satellite altimeter Jason-1and Jason-2 as well as the data measured by buoys. It was found that there was a s ystematic error of 0. 3—0. 4 m between SWH obtained from HY-2 and other altimeters and buoys. The comparison indicated that there was a linear relationship between the data of HY-2 and buoys,which could be used to calibrate SWH measured by HY-2 to better reflect the real marine situation. Comparisons show that the results are good and HY-2 altimetry data work well with other satellite altimetry data.
摘要:To make better use of wind streak information from Synthetic Aperture Radar( SAR) images used in turn to obtain high-precision sea surface wind fields,wind direction is retrieved using decimated wavelet transform associated with two-dimensional Fast Fourier Transform( FFT) with different modes and Advanced Synthetic Aperture Radar( ASAR) images of various p olarizations. Finally,the resulting wind direction is compared with those obtained using traditional FFT and the i mproved local gradient method. The results demonstrate the feasibility of applying the wavelet transform algorithm to sea surface wind direction inversion. Multi-resolution analysis of ASAR images with time-frequency window of wavelet transform enables more precise determination of wind direction compared with traditional Fast Fourier transform and improved local gradient method. In addition,the inversion results contain certain differences in diverse modes and various polarization data.