Interferometric Synthetic Aperture Radar (InSAR) is a widely used technology of radar remote sensing,which can potentially measure centimetre-scale changes in deformation over timescales of days to years,and becomes a hotspot of SAR image applications.In this paper,for the problem of InSAR technology for surface deformation monitoring,we analyze the new progress of multi-baseline DInSAR methods in recent years,and discuss the principles and applications,including coherent target DInSAR,distributed target DInSAR,SAR tomography,and so on.Moreover,the trends of surface deformation monitoring using DInSAR,which develop from two dimension parameters to three dimensional four-dimensional,and space from city area to broad non-city area in detail.
Many achievements have been presented for retrieving land surface temperature (LST) from thermal infrared satellite sensor data.The accuracy of retrieval results can reach 1K or less.But thermal infrared remote sensing is so strictly influenced by cloud that it can do nothing in cloudy weather.However,microwave remote sensing is influenced slightly by cloud and atmosphere.Passive microwave emission can penetrate non-precipitating clouds,thereby provides a better representation of LST under nearly sky conditions.It is also multi-polarized.Passive microwave remote sensing holds a unique advantage in retrieving LST.Retrieving LST from passive microwave satellite sensor data has become one of the most important problems in passive microwave remote sensing.This paper introduces radiance transfer equation and model in retrieving LST from passive microwave,parameterized forward model and main algorithms of retrieving LST from passive microwave,including statistical algorithms and physical mechanism algorithms.Lastly,the weakness and difficulties for retrieving LST from passive microwave at the present study stage are analyzed.It may be very helpful to the future researches related to temperature based on microware remote sensing.
According to characteristics of soil salinization in the western Jilin province,four regions are selected as study areas and these regions are non-destructive detected by dual-band board radiometer with multi-angle and dual-polarization mode.Based on the field data,Levernberg-Marquardt Algorithm was selected to calculate the imaginary part of soil permittivityand Dobson model as the real part of soil permittivity.Then,the dual-band different method was used to study the relationship of soil permittivity and salinity,water content.The difference of the real part of soil permittivity between two frequency (band) is well linear relationship with soil water content,while has the difference of the imaginary part with salinity.This result is significant on the inversion of soil salinity characteristics,and laid a theoretical foundation for soil salinity quantitative inversion from microwave remote sensing image.
Researches of ship detection using Synthetic Aperture Radar (SAR) images have received considerable attentions in the area of marine.The problem of the detection and accurate positioning of ship wake linear feature in the SAR ocean imagery is one of great difficulty.This paper discusses the detection of ship wakes,according to the study on ship wake detection technology in SAR imagery in recent years,this paper summarizes the kinds and imaging mechanism of ship wakes,the factors that would affect ship wake detection and the all kinds of wake detection algorithms are analyzed,and obtains the merits and demerits of algorithms.The future direction of research and development is viewed.
Endmember's number in traditional endmember extraction algorithms always need to be specified manually,which will result in multiple or miss chosen of endmembers.To solve this problem,this paper propose a new endmember extraction algorithm based on some characteristics of data field,such as natural topological clustering and visualization.Firstly,partial original images into several zones calculate potential of all pixels in each zone based on the theory of data field.Then select a certain number of feature points in each zone and combine all the feature points into a feature image,and then calculate data field of the feature image.Finally,according to the structure of topological clustering in data field,endmembers can be extracted visually.At the same time,the best endmembers position can be gained.Using the data of AVIRIS obtain in the Cuprite mining area,endmembers of high quality are extracted.The results show that the method is reasonable and effective.It demonstrates that the proposed algorithm can be applied to endmember extraction of hyperspectral images.
Retaining spectral and high resolution are the important issue of image fusion.An image fusion method based on Region Segmentation and Wavelet transform for multi-scale remote sensing image fusion is proposed.Firstly,images must be corrected,then the multiscale decomposition of source images is carried out with the wavelet transform.Then region segmentation based on area standard deviation is done for the low-frequency coefficients,which is decomposed two parts:target information and background information.The target information is fused with larger absolute value operator,and the background information is fused with gray error value operator.The high-frequency coefficients are fused with image definition.Finally the fused coefficients are reconstructed to obtain the fusion image.Compared with several traditional methods,the results show that the fused image by the presented algorithm can not only hold spectrum information of the multispectral image,but also improve the high resolution of the fusion image,then preparing for information extraction and image classification.
For the classification among different land-cover types in a hyperspectral image,particularly in the small-sample-size problem,a feature extraction method is an approach for reducing the dimensionality and increasing the classification accuracy.A Supervised Principal Locality Preserving Projection (SPLPP ) feature extraction algorithms,which uses the label information of training sample in Locality Preserving Projection (LPP),was proposed in this paper.Three main steps are involved in the proposed SLPP:firstly,use PCA to remove redundant information,and then combine the label information in LPP,finally,SPLPP projects high-dimensional hyperspectral image into a low-dimensional space.Last but not least,SPLPP uses the extracted features as inputs of classifiers (e.g.Support Vector Machine (SVM ) and K-Nearest Neighbors (KNN )) to do classification.The experimental results show that the proposed SPLPP has better local information retention ability and class discrimination ability,compared with PCA,LPP,LDA.
Sea reclamation works and fish farming are increasingly common in coastal zones,and how to accurately and rapidly extract the coastal aquaculture area is important for the development of coastal zones.This paper discusses a CBR (Case-Based Reasoning) method.Firstly,using a 10 meter resolution of a multi-spectral remote sensing image of Eastern Guangdong over ten thousands of spatial,spectral,shape and texture features were extracted based on the 1∶50 000 standard framing of land use thematic data and by using image analysis.Then nine optimized features were selected using the Principal Component Analysis (PCA) and the construction of a case base was accomplished based on these.After that,a multi-scale image segmentation was performed on the 2.5 meter resolution of a fused image of the test area,which is located on the Western Guangdong coast,and CBR classification was applied on all the segmented image objects.In the end,the classification accuracy was evaluated.The CBR classifier classifies an aquaculture area within coastal belts with an accuracy of 84.56%,in contrast to that the accuracy of the Maximum Likelihood Classifier (MLC) is 82.5%.The CBR method outperforms the MLC by 2.2% in prediction accuracy.The advantages of the CBR approach are obvious,particularly in the areas that are far away from the coastlines.In conclusion,the CBR approach could be successfully applied to the extraction of coastal aquaculture areas.
Based on eco-geographical regions,this paper,from two aspects of area accuracy and position accuracy,quantitatively studied the uncertainty of five global Land Use and Land Cover(LULC)data sets in practical application.The conclusions of this paper can be employed in choosing reasonable data sets efficiently for other relative researches about eco-geographical regions.In addition,the results will also provide reference for Chinese researchers to make global LULC map.In this paper,twenty typical eco-geographical regions were chosen as research objects.On the one hand,in the estimations of thematic type area,the uncertainty degree and reasons of each data set were analyzed through counting the minimum error of each data set;On the other hand,error matrix was adopted to study the position uncertainty of each LULC type in five data sets,reasons and spatial distribution laws.The results indicated that,in eco-geographical regions scale,these three data sets of MODIS,Meris300 and Glc2000 were super to Umd and Usgs.And for the distribution of the eco-geographical regions from south to north,from east to west,the uncertainty of these three data sets would be decreased gradually Meanwhile,Meris300 data set estimated the area and position of each LULC type with the highest stability,while the accuracy was not the highest.It was good at the estimation of construction land and water.Both estimation stability and accuracy of MODIS data set were acceptable and it was used to estimate farmland with lowest error.Glc2000 data set was more suitable to be applied to study the eco-geographical regions with simple LULC type.
Fully polarized microwave radiometer plays an important role in remotely sensing sea surface wind vector from space in the future,and it can provide crucial valuble information for short-term weather forecast,meteorological and oceanographic studies.Fully polarized radiometer's simulation based on computer software platform is significant for system design and fully polarized calibration method simulation validation.System simulation model of polarization-correlating microwave radiometer is established by ADS software,and then its effectiveness used for fully polarized calibration method is validated in terms of the relationship between input stokes vector and output voltage vector.Based on the system model,calibration method of fully polarized calibration standard is validated via noise injection method.Simulation results show that the system simulation model is valid and can be served as a reference for system design and fully polarized calibration method simulation validation.
Atmospheric water vapor is one of the main factors when satellite infrared radiometer measure sea surface temperature.In this paper,the correlation among sea surface temperature and its retrieval error and atmospheric water vapor are analyzed by the monthly mean data of sea surface temperature and water vapor dataset in January 1988~November 2009,60°N~60°S and AVHRR quasi synchronous pathfinder matchup database.The results showed that the latitudinal monthly mean value of both sea surface temperature and precipitation water vapor have significant seasonal variation characteristic;in the higher latitudes,show the smaller deviation between remote sensing retrieval sea surface temperature and in-situ sea surface temperature,sea surface temperature is usually underestimated in middle-latitude and low-latitude zone,but overestimated in high-latitude zone based on PFSST algorithm.Climatological monthly mean value of sea surface temperature retrieval error have a good seasonal distribution in middle and high latitude ocean,and has higher correlation among it,sea surface temperature and precipitation water vapor on time series statistics.
The successful launches of many new satellite sensors have brought a new hot topic of comparison in different sensors.Firstly,the study conducted the sensors characteristics by comparison between IRS-P6 LISS-3 and Landsat-5 TM,including orbit characteristic and spectrum.Secondly,this three synchronous sensor imagery of two sensors were obtained as material,and the comparison analysis was carried between corresponding V-NIR and SWIR bands of two sensors.And then,a conversion equation was developed between TM and LISS-3.Finally,a comparative analysis about this formula was made with the Chander’s validate the conversion equation.The results showed that the determinant coefficient comparison between actual TM and simulated TM were remarkably improved before conversion,Coefficients of determination were all greater than 0.97.Some comparisons were based on Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) respectively,were carried out using actual TM and simulated TM data.These determinant coefficient comparisons between actual TM and simulated TM were a certain improved before conversion,and these scatter spatial distributions of MNDWI and NDVI were more reasonable and symmetric uniform.Therefore,it is feasible and effective to provide relation equations.This effect is batter than conversion equations from Chander.
This paper analyzes the eigenvalue decomposition,all combinations of scattering mechanisms and polarization characteristic spectrum,and then proposes a method of false color composition based on three kinds of characteristic spectral parameters,which can be more effective directly reflect the scattering feature.Then,the scattering entropy,scattering angle,anti-entropy and four parameters of polarization characteristic spectrum area are studied in feature space.And this paper gives the best options for multi-dimensional feature vector,in order to achieve the traditional classification algorithm to process polarimetric SAR images,such as ISODATA.This study selects a scene of Radarsat-2 polarimetric SAR data for test,including typical urban,vegetation and water.The experimental results show that the false color composition with polarization characteristic spectrum reflects the local feature of the scattering material.The unsupervised classification with three of characteristic spectrum and scattering angle show that the proposed method overcome the defects in H-Alpha plane,which causes blurring segment between city and the vegetation.Classification accuracy is higher than the unsupervised classification with H-Alpha plane,but has the similar effect with Wishart-H-Alpha-A classification algorithm.
Synthetic Aperture Radar(SAR)is an active earth observation technology,has been widely used in disaster reduction field at home and abroad.“Environment and disaster monitoring and prediction small satellite constellation” of HJ-1C is the first civil SAR satellite,and is the first satellite used for disaster reduction and relief field in china,has been lauched successfully on November 19,2012.Before launching,from the SAR technical characteristics,this paper analyzes the application situation and potentiality of SAR data divided into single-polarization SAR,multi-polarization SAR,Interferometric SAR and Polarimetric SAR Interferometry.This study has important significance in promoting deeper development of disaster monitoring and warning business system by SAR technique,driving application of high space technique and pushing forward SAR satellite constellation construction of disaster monitoring and warning field in China.
Sea ice is a crucial component of global climate system,which is an effective index of global climate change.HY-2 (HaiYang-2) satellite is the first satellite of China particularly used for monitoring Ocean environmental dynamics.With the launch of HY-2,valuable oceanic data are obtained day and night,regardless of could cover.Thus,how to use HY-2 satellite to monitor polar sea ice is needs to be solved urgently.A new sea ice concentration retrieval algorithm is advanced based on radiative characteristics of water and sea ice.The new algorithm is comparable to NT2 algorithm and ABA.With this algorithm,sea ice concentration could be retrieved successfully using HY-2 passive microwave data.
The SEBS model provides a new method for studying the plateau non-uniform surface regional evapotranspiration estimation and some vapotranspiration reference for the sparse areas of high altitude meteorological stations.The evapotranspiration of Naqu county was estimated by using MODIS data and meteorological observations based on SEBS model in 2010,and analyzed the evapotranspiration with relationship between the meteorological factors ,and the NDVI.The results show that the evapotranspiration of Naqu county in spring and summer is high,autumn and winter is low,the south and the northeast water in the study area are large evapotranspiration,in central and the northwest evapotraspiration is smaller.The temperature and the surface temperature effect is obvious to evapotranspiration,the increasing of air temperature and surface temperature lead to evapotranspiration increase;and the NDVI has different season variations,NDVI and evapotranspiration change were the largest in summer,they are minimum in winter.The SEBS model is of adequate accuracy in estimating the evapotranspiration of Tibetan Plateau and can be applied to estimating the daily evapotranspiration on a regional scale.
The application of Landsat satellite image in crop-growth monitoring is limited by the 16-day revisit cycle and frequent cloud contamination.One solution is to combine the high-spatial information of fine-resolution sensors,such as Landsat with the high-frequency temporal information of coarse-resolution sensors,Moderate Resolution Imaging Spectroradiometer(MODIS).In the present paper,we studied the adaptability of Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM),which is a spatial and temporal fusion technology in monitoring crop in Shihezi of Xinjiang Uygur Autonomous Region.We analyzed the correlation between fusion result and observed data.The result showed that the correlation between fusion image and real image was high,however,due to the change of land cover which did not meet the assumption of STARFM,and the differences were still significant,especially for the reflectance of fusion image and real image.The correlation between fusion image and real image was low,when feature type changes.
The temporal and spatial distribution dataset of the coal mining area in Wuhai were extracted by multi-phase Landsat MSS / TM remote sensing data since 1979 and the characteristics of spectral features of coal mining areas,which the reflectance of the each band coal mine in the remote sensing would be far lower than that of other surface features.Meanwhile,the dynamic changes in coal mining areas and its influence on ecological environment in Wuhai were monitored based on SPOT-Vegetation NDVI time-series dataset.The results indicated that the coal mining area in Wuhai increased from 2.69 km2 to 109.34 km2 during the last 30 years.and large area of cultivated land,woodland and grassland were encroached with the dilation of coal mining and led to a rapid deterioration of the ecological environment in Wuhai.The NDVI declined from 0.2043 in 1998 to 0.1231 in 2001.After 2000,although the miming area in Wuhai tended to increase,the vegetation cover improved obviously,especially during 2005~2010.The NDVI of Wuhai increased from 0.1417 in 2005 to 0.2028 in 2010.
The wakame raft cultivation monitoring is an important part of remote sensing monitoring in Dalian.It's very necessary for local government to find out the position,range,quantity of the raft cultivation and grasp industrial development information by seeking an accurate and fast interpretation method.The wakame raft cultivation interpretation method is studied,by using Landsat TM data source,in the view of data mining association rule analysis technique,in Dalian Jinshitan Beach sea area,and the raft cultivation interpretation workflow is proposed based on association rules.The wakame raft cultivation area and sea classification knowledge are identified by using data mining association rules analysis.The raft cultivation could be interpreted with the classification rules.In order to eliminate the noise spots,in the context of morphological characteristics and cultivation habits of the wakame floating raft,the final classification binary results is obtained,The method of the overall recognition accuracy reaches 80%,which is compared with visual interpretation result,the interpretation accuracy rate is improved by 11.64%.The method can meet the basic requirements,which is feasible for monitoring.
The water level amplitude of Poyang Lake changes greatly lead to severe drought and flood disaster.It is of great significance to real-time monitoring water body.Using MODIS remote sensing data to water dynamic monitoring brings obvious advantages since it has high temporal resolution.Water extraction method varied,and the improved normalization water index (MNDWI) method is chosen for Poyang lakes water dynamic monitoring after comparing the different water extraction methods.We establish the area-water level quadratic function model by utilizing 40 phase of MODIS data from 2000 to 2010 to study the evolutionary process of Poyang Lake in these ten years and simultaneously by combining the water level data of this period.With the measured data validation,the area-water level relation by interpretation of remote sensing has a good consistency with that established by topographic analysis,which provids effective measures for hydrological features analysis of Poyang Lake and the basis for flood control and drought resistance.
Ground spectrometer ASD FieldSpec Pro FR was applied to sequentially measure the spectra of healthy and withered mason pine needles.Time series analysis of spectra,a sensitive period and sensitive characteristics identification were conducted in the study.This paper showed that compared with the regular spectral series of healthy mason pine,the spectral series of mason pine infected by pine wood nematode (PWD) appeared none regularity.Through time series analysis of spectral derivative of PWD,we found that two phases spectral variation were showed,first,the spectral reflectance reduced in the red edge,and then the blue edge shift to red edge.In the early September,the mason pine infected by PWD have shown the typically spectral characteristics of pathological vegetation.Finally,by comparing the rate of change in defined hyper-spectral parameter values,the largest first order differential valves within near-infrared spectrum,and the ratio of first order differential sum in the red edge (SDr) to first order differential sum in the blue edge (SDb) are significant indicatives of PWD occurrence.
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