Issue Ebookcover storyIssue Cover
Latest Issue
Online FirstList of IssuesDocument Types
2016
2015
2014
2013
2012
2011
2010
Issue 6
Issue 5
Issue 4
Issue 3
Issue 2
Issue 1
2009
2008
2007
- Abstract：Besides amplitude, frequency and phase, the polarization is another basic property of the electromagnetic wave. In the remote sensing field, the polarization is mainly applied in active detection systems of radar and lidar. This paper presents the quantitative relationship between soil moisture and polarization signatures in a certain type of soil in a farm. And this relationship is expected to be introduced on agriculture and hydrology ultimately. The experiments were performed both in the laboratory and the field. Soil samples with different moisture contents were measured at three wavebands on visible spectrum, and at several viewing angles in the plane of incidence. The polarization signature was indicated by the multi-band and multi-angle degree of linear polarization （DOLP） in this paper. The soil moisture were divided into five levels according to the properties of DOLP curves, namely, the quasi-quantitative relationship between soil moisture and its polarization signature were established. The percentages of soil moisture of the five levels are: ≤10%, 10%—20%, 20%—40%, 40%—56% and >56%, respectively. Although this division for soil moisture is on a rather large scale, it will meet the precision of application agricultural and hydrologic remote sensing.203|353|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：Spatial scaling for net primary productivity（NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions.How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem.Nonlinearity and effects from land cover type are two main problems in NPP scaling.In this paper,the contextural approach based on mixed pixels and support vector machine（SVM） algorithm are used to make the scaling model from the fine resolution（TM） to the coarse resolution（MODIS）.Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed,and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished.The result shows that the correlation between Rjcorrected of the correction factor for scale effect and 1-Fmiddle density grassland estimated by SVM regression model is higher（R2=0.81）.Before the correction for scale effect,the correlation between NPPMODIS and NPPTM is lower（R2=0.69;RMSE=3.47）,while the correlation between NPPTM and corrected NPPMODIScorrected is higher（R2=0.84;RMSE=1.87）.Therefore,NPP corrected for scale effect has been greatly improved in both correlation and error.Keywords：net primary productivity;light use efficiency model;remote sensing;scaling;support vector machine309|2576|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：This paper proposes a generative model based semi-supervised learning method of remote sensing image classification,which makes use of both the labeled and unlabeled samples to handle the insufficient labeled training samples problems.We first train an original classifier by the small number of labeled samples alone.Then we re-train it by both the labeled and a large amount of unlabeled samples.This process is iterated until the likelihood function of all the samples are converged to the local maxima.Through the designed experiments of the two different mixture models,It is found that the unlabeled samples help us to get the method to enhance the classification performance to a large extent on condition,which the ratio of the unlabeled samples to the labeled ones must be appropriate.Thus,we have also compared the method by using the state-of-the-art support vector machines（SVMs） with the same labeled samples,of which results show that our method works better.Keywords：remote sensing classification;semi-supervised learning;Expectation Maximum（EM） algorithm321|1769|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：This paper proposes a novel work mode of spaceborne ScanSAR with multiple azimuth beams,where the range swath coverage can be greatly extended while the azimuth high resolution remains unchanged.Based on the analysis of the characteristics and main phase errors of this mode,the compensating methods for these errors are given.Then the echo data simulating methods for this mode are introduced and the corresponding imaging algorithms are discussed as well.Thus the feasibility of this mode and the validity of the compensating methods are proved by simulation.Keywords：CS;DPCMAB;RD;ScanSAR;SPECAN244|794|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：Fragmentation is often used to describe spatial structure of earth landscape ecology.This paper introduces the fragmentation in remote sensing sampling survey,and focuses on the application of area-scale and fragmentation in the stratified sampling.Experimental results show that:（1） in the regions with broken planting structure,correlation coefficients of area-scale and fragmentation are both above 0.7.Therefore,they both can be used as auxiliary variables for stratification design in such regions.With the increasing of sampling grids,correlation coefficient of area-scale increases,while correlation coefficient of fragmentation decreases.When sampling grid size is smaller than 100m×100m,fragmentation is better than area-scale.（2） in the regions with regular planting structure,area-scale is always better than fragmentation.Fragmentation is more suitable for the application of winter wheat area sample survey in regions where planting structure is broken,sampling units is small.Keywords：winter wheat area;auxiliary variable;area-scale;fragmentation;Correlation coefficient289|1423|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：Object-oriented classification has been paid more attention in the field of remote sensing.In this paper,a novel object-oriented algorithm based on rough set theory is proposed to classify different objects extracted from high-resolution remotely sensed imagery.The method consists of three steps.Firstly,image segmentation is achieved by watershed transform based on phase congruency gradient and foreground marking to extract image objects.Secondly,texture vector of each object is obtained by Gabor wavelet,and clustering rules is further formed based on the knowledge reduction theory.Finally,according to the restriction of the preliminary clustering result derived from spectral feature of objects,the ultimate classification is achieved referring to the rules.Meanwhile,a new technique to discretize continuous interval-valued attributes is developed,which is very suitable for the object-oriented classification,because the rough set is inadequate for dealing with continuous attributes.The experiments demonstrate that the proposed method can achieve better results and better accuracies.Keywords：object-oriented classification;rough set;watershed transform;Phase Congruency;Gabor wavelet;discretization323|2479|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：The method to rebuild space-borne pseudo-range measurements from real-time position data is discussed first in this paper.And then,the accuracy of dynamic orbit determination using rebuilt pseudo-range measurements and precise GPS ephemeris is analyzed in detail.Data from GRACE-A satellite from March 1 to 14 in 2008 is processed for this purpose.Research results show that the accuracy of real-time single point positioning using C/A code pseudo-range measurements and GPS broadcast ephemeris is about 15m,and the accuracy of dynamic orbit determination in post-processing mode is about 2m.It is demonstrated that dynamic orbit determination using rebuilt pseudo-range measurements in post-processing mode can not only significantly improve the position accuracy of real-time navigation solutions,but also provide consecutive satellite orbits.Keywords：rebuilt pseudo-range measurement;orbit determination;LEO;single-frequency;broadcast ephemeris;precise GPS ephemeris220|924|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：A model analyzing the effect of baseline oscillations on InSAR phase error is presented in this paper.By a baseline oscillations model,which includes horizontal oscillation and perpendicular oscillation,the models for SAR complex image of slave antenna and interferometric phase error are both deduced.Then effect of baseline oscillations on interferometric phase and quality of SAR image is analyzed.At last,a computer simulation on SRTM system is given.Through raw signal simulation of InSAR in time domain,raw signal for point target and extended scene targets are simulated.After imaging,complex image pairs and interferometric phase are obtained.The simulation results proved the validity of analysis.Keywords：interferometric SAR;interferometric phase;baseline oscillations;raw signal233|782|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：The paper proposes a parameterized model based on the vegetation canopy radiative transfer model SAILH.This model simplifies the calculation of the nine intermediate variables of the SAILH model,and adapts an explicit formula to calculate the contribution of the single scattering in the illuminated canopy.We evaluate the retrieval accuracy and efficiency of the parameterized model with simulated data and ground-based measurements which are taken in the satellite-aircraft-ground synchronous experiment over the Heihe river basin in 2008.The results shows that the retrieval efficiency is improved greatly by the parameterized models but the retrieval accuracy is kept.It is also found that the stability of the parameterized model is better than the SAILH model.Keywords：SAILH model;parameterized;LAI retrieval258|1076|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：This paper proposes a change detection algorithm for remote sensing images based on image fusion and adaptive threshold selection.An improved image fusion technology has been employed in processing difference image and ratio image of original data in order to construct the fusion images.Based on these images,a coarse range of change threshold has been got from an adaptive iterative operation.Then,after analyzing the discrete levels of the image pixels distributed on both sides of the threshold range,the final threshold range has been achieved.Thus much more optimal change threshold helps to extract the final change region.the experimental results in the paper suggest that the detection accuracy of this method,which has certain stability and intelligence outperform the traditional change detection methods.Keywords：change detection;image fusion;adaptive selection;threshold range313|2231|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：This paper presentes an in-flight site calibration method for thermal infrared band of HJ-1B,taking advantages of Lake Qinghai test-site for the absolute radiometric calibration for thermal infrared band of HJ-1B.By using measurement data and landsat5-TM to analyze the different results between site calibration and primeval calibration,it shows that the accuracy of in-flight site calibration had been increased 0.6%—3% comparing with the primeval calibration result and the total accuracy is about 1K.Thus this new site calibration method is applicable to the in-flight absolute radiometric calibration of these sensors without the capability of detecting cold space.By the way,the accuracy of calibration for thermal infrared of HJ-1B can meet user’s requirements for the application of quantitative thermal infrared data.Keywords：HJ-1B;thermal infrared;site calibration;accuracy314|1545|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：The choice of optimization method is very important in the assimilation process of crop growth model and remote sensing data,and it concerns the running efficiency and result accuracy of assimilation.In this study,a new optimization-Particle Swarm Optimization（PSO） technique is used for assimilating remote sensing data and RiceGrow model in minimizing difference between inverted and simulated values by remote sensing and RiceGrow model.We compare PSO with another optimization-Simulated Annealing（SA） and explored the assimilation result when LAI and LNA are used as external assimilation parameters respectively.The results show that PSO performed better than SA in both running efficiency and assimilation result,which indicates that PSO is a reliable optimization method for assimilating remote sensing information and model.LAI and LNA each have advantage as external assimilation parameters,sowing date and seeding rate can be well inverted when LAI is selected as external assimilation parameter,while nitrogen rate is better predicted using LNA.However,the inverted result is better when LAI is employed as external assimilation parameter.Experiment data is used to test the assimilation technique and result shows that the relative errors for initial parameters of growth model and yield are less than 2.5% and 5%,respectively.RMSE values are between 0.7 and 2.2,which indicates that the assimilation technique based on PSO is reliable and applicable and that this new assimilation technique can lay the foundation for crop model application from spot to region scale.Keywords：particle swarm optimization;RiceGrow model;assimilation technique;parameter initialization232|1400|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：This paper proposes a new unmixing method based on the simulation of real scenario.Fractions of the components are firstly obtained through the real scenario simulation.Then reflectance values of the endmembers（simulated endmembers） are calculated by combining the image reflectance values and corresponding simulated fractions.A constrained linear model is finally used to unmix pixels based on the simulated endmembers.Comparative analysis of the different endmember extraction methods,such as simulated endmembers,image endmembers,and reference endmembers,indicates that the simulated endmember method has the highest estimation accuracy and robustness for the crown closure of moso bamboo.The advantage of the real scenario simulation is to use field data as a priori knowledge for endmember extraction and introduce a three-dimensional simulation model into a two-dimensional linear spectral decomposition.Keywords：real scenario modeling;simulated endmember;spectral mixture analysis;linear mixture models;Landsat TM361|1479|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：This paper proposes a fusion method by combining generalized intensity-hue-saturation（GIHS） transformation and maximum a posteriori（MAP） analysis in order to improve the fusion quality of multispectral（MS） and panchromatic（Pan） images from a new type of remote sensing platforms.The intensity component of the MS images is first obtained by GIHS transformation.Then a new Pan image is acquired by combing the intensity component and Pan image using a steepest-descent optimization algorithm based on the MAP framework.Thus,the fused images are obtained by the GIHS method.Experiments are conducted using the IKONOS MS and Pan images,and Quickbird MS and Pan images.The proposed method is compared with the GIHS fusion method,the wavelet transform（WT） fusion method,and the combined WT-GIHS fusion method.The experimental results show that the proposed method can achieve better fusion result than exiting fusion methods.403|1356|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
- Abstract：The relevance vector machine（RVM） is used to process the hyperspectral image in this paper to estimate the classifiers precisely in the high dimensional space with limited training samples.The detail of RVM is firstly discussed based on the sparse Bayesian theory.Then four multi-class strategies are analyzed,including One-vs-All（OAA）,One-vs-One（OAO） and two direct multi-class strategies.In the experiments,the multi-class strategies are compared and RVM is further compared with several classical classifiers,including the support vector machine（SVM）.The experiments show that two direct multi-class strategies occupy too much memory space with low efficiency.OAA has the highest precision,but is low in efficiency.OAO is the best in efficiency and the precision approximates to OAA.Compared with SVM,RVM is low in precision,but sparser than SVM.The sparse property is important when the test set is large,which makes RVM suitable for classifying the large-scale hyperspectral image.Keywords：remote sensing;classification;relevance vector machine;hyperspectral327|1091|0<HTML><L-PDF> <Meta-XML>Updated：2021-06-10
