With the technical promotion of Synthetic Aperture Radar (SAR),the study on vessel detection and classification using current new generation (high resolution and multi-polarization)SAR image has become a hot research topic for maritime traffic monitoring,fishery control and other applications.Techniques of vessel classification with SAR image are reviewed in this paper.Based on the researches in recent twenty years,image features (including geometric features,backscattering features and polarimetric features)for vessel classification are summarized;furthermore,merits and demerits of them are pointed out.The Vessel classification algorithms with SAR image are also investigated and summarized.Finally,some comments and suggestions are given for the development of vessel classification application.
With the fast development of Synthetic Aperture Radar Interferometry(InSAR)technology in the field of topographic mapping and the ground surface deformation monitoring,there is a much higher demand in the improvement of its measurement accuracy.As is well known,atmospheric effect is the main factor to limit the accuracy of the InSAR displacement measurements,therefore,the removal of the atmospheric delay becomes a crucial problem to be solved.In this paper,we describe the existing methods of atmospheric correction in InSAR technology and also pointed out their advantages and disadvantages separately.The existing methods mainly contain the correcting methods with the SAR images only and the correcting methods with the external water vaper sources(e.g.surface meteorological data,MERIS,MODIS,GPS and the atmospheric numerical model).Finally,we also discussed the difficulties and challenges in this field at present and the possible improvement of the atmospheic correting methods in future.
Based on the object\|oriented land cover classification technique from eCognition and the corrected Landsat TM data,this paper acquired the land use/cover data of the study area in 2010.Then the data was further divided into coniferous forest,broad\|leaved forest,mixed broadleaf\|conifer forest and non\|forestry land.Meanwhile under the ArcGIS platform,six bands of reflectance values,ten kinds of vegetation index including RVI,NDVI,SLAVI,EVI,VII,MSR,NDVIc,BI,GVI and WI,and the topographic factors as DEM,ASPECT and SLOPE,were calculated to analyze the correlations between the corresponding forest LAI and measured using TRAC with each factor.Then compared the model performance of multiple linear regression with partial least squares method,this paper established the optimal model to retrieve the forest LAI of each forest type.At last,the distribution map of forest LAI in this study area was made by integrating remote sensing inversion model with the forest classification data acquired beforehand.
Validation is necessary before quantitative application of the satellite remote sensing products,Satellite radar altimeter can detect marine dynamic environment parameters,including sea surface wind speed and significant wave height,which can be verified with different space and time matching methods,Jason2 satellite altimeter sea surface wind speed and significant wave height products detected in 2011 has been verified using in\|situ data downloaded from National Date Buoy Center (NDBC) by three different space and time matching methods.The RMSE of Jason2 sea surface wind speed and significant wave height are 1.28 m/s and 0.28 m,respectively.The result indicated that the reasonable verification results of satellite altimeter sea surface wind speed and significant wave height can be calculated in certain time (1 h) and space (50 km) limit between satellite and in\|situ data; and the accuracy of Jason2 sea surface wind speed and significant wave height which is relatively higher in the Gulf of Mexico and the western Atlantic Ocean is different in different ocean.
Short-Wave InfraRed(SWIR)Band located at 1.6 μm and 2.06 μm can be used to detect the CO2 information at low atmosphere.Apart from that of atmospheric compositions(mostly CO2),satellites measurements in the SWIR are also influenced by scattering of atmospheric molecular,aerosols and clouds.Therefore the aerosol and its vertical distribution could reduce the accuracy of total column CO2 from the GOSAT.In order to improve the accuracy,the sensitivities of retrieval algorithm with AOD,types and its profiles have been analyzed by a forward radiative transfering model\|DISORT.The results show that:①The impact of AOD ,depending on the phase function and single scattering albedo,has different behaviour for different aerosol types,and the variation CO2 column amount with the increase of AOD is also different;②When aerosol type changes,the CO2 column amount varies,and this variation increases with AOD;③For dust aerosols,of which the vertical distribution decrease exponentially with height,indexed by a scale height,underestimation of scale height will result in overestimated CO2 column amount,while aerosols concentrated within a limited height,underestimation of aerosol height will result in underestimated CO2 column amount,and the higher the aerosol layer,the smaller of CO2 column amount will be derived.
Remote sensing is one of the important Methods for the large\|scale ecological research.The relationship between terrestrial plant community characteristics and their spectral characteristics is the key to interpret remote sensing images.Because of its high spatial and spectral resolutions,ground experimental data would be an accurate reflection of spectral information of surface features.So as a good guide for satellite remote sensing interetation,ground experimental data can provide supporting data for remote sensing monitoring of grassland degradation and building of grassland model.This study selected the dominant vegetation types of the Naqu Prefecture in Tibet ,the canopy spectra measuring by ASD Field Spec 3.One of the dominant species ,as an example measured in different coverage of different period.The results show that:①Canopy spectra of different vegetation communities with special spectral curve,the visible bands spectral reflectance turn Stipa purpurea,Kobresia pygmaea ,K.tibetica,near\|infrared bands spectral reflectance in turn is K.pygmaea,K.tibetica,Stipa purpurea;red edge position can be identified the K.tibetica,but not be able to distinguish between K.pygmaea and Stipa purpurea;②The position of K.pygmaea's red edge,green peak does not change with the increase of coverage;Continuum removal absorption depth and area increased with degree of coverage;③In the recession period,the visible and red edge band,Canopy spectral reflectance decreases with the reduction of the chlorophyll content decreased,becoming “Red edge blue shift,the green peak decline”.
Drought is one of the most catastrophic natural disasters in human history.Soil moisture is the key parameter in study of drought monitoring.Application of remote sensing techniques for estimation of surface soil moisture can adequately reveal the spatial and temporal variations,which is suitable for a large scale dynamic monitoring.Based on Landsat TM data,land surface temperature LST was derived by means of universal single\|channel algorithm.EVI was acquired to build the LST\|EVI feature space,and calculated the temperature vegetation dryness index(TVDI).TVDI and observed soil moisture data were analyzed and a linear curve was fitted to estimate volumetric soil moisture content in the depth of 20cm for June 14th,2010,in areas of natural surface in Huanghua City.TVDI is feasible for monitoring soil moisture content of the study area.Spatial distribution of volumetric soil moisture content varied significantly.Area of moderate soil moisture was the largest.Areas with critically low soil moisture content mostly were upland fields,which is located in the southwest and part of the north region.However,the areas with high soil moisture content mainly located in the reed swamp and coastal bare land.
Superlarge and large magmatic deposits have frequently been discovered in the wall rock near the small intermediate-acid intrusions.Therefore,small intermediate\|acid intrusion-related deposit is a great direction for ore exploration.However,small intermediate\|acid intrusions were often omitted because of the low level of geologic research which seriously restricted the discovery of the relevant mineralization.During the mineral exploration at Jiafushaersu area,foot wall of Darbut Suture,West Junggar,Xinjiang,a mineral exploration model dominated by remote sensing technique was constructed for small intermediate-acid intrusion\|related deposits.In this model,through ASTER data-based image processing and interpreting,small intermediate\|acid intrusions were identified.Furthermore,remote sensing anomalies were extracted using principal component analysis technique.The ore prospecting targets were shed light on by synthetic analysis based on remote sensing information,geologic and geochemical data.A porphyry\|type mineralization point,Jiafushaersu Mo mineralization point,was discovered during ground inspection which has confirmed the validity of this method.The method can provide a reference for the mineral exploration of small intermediate-acid intrusion\|related deposits in the western regions.
Vegetation plays an important role in the climate change,and climate change affects vegetation growth and its photosynthetic activity in return.In this study,we combined datasets of satellite\|derived Normalized Difference Vegetation Index (NDVI) from 2001 to 2010 and climatic factors to analyze spatiotemporal patterns of vegetation growth and its responses to temperature and precipitation over Yimeng Mountainous Area which is the typical region of rocky mountain areas in Northern China.To clarify the trends of the temperature and precipitation,datasets from 1980 to 2010 are used.Our results show that vegetation growth appeared a trend of improvement in the past decade,and the slope of the Max\|NDVI is 0.0026 over Yimeng Mountainous Area.In past decade,the significant area of vegetation degradation accounted for 10.52% of the total study area,however,vegetation growth over 28% of study area was well\|grown significantly.In the annual scale,the benign development trends of vegetation condition from different seasons were the same as annual trend,especially in the summer and autumn.Datasets from weather stations show that inter\|annual variation of vegetation growth has a weak relationship between annual precipitation and mean annual temperature.In monthly scale,although vegetation growth correlated better with temperature than precipitation,the responses in various regions were different to monthly variations of temperature and precipitation.Overall,there are significant variations between the spatiotemporal patterns of Yimeng Mountainous Area and responses to temperature and precipitation.What’s more,this study found that temperature may be the major effective factor which affects vegetation growths in Yimeng Mountain Area.
With the rapid development of society and economy,the increasing demand for coal resources not only caused the over\|exploitation,but also damaged the ecological environments.So it is more and more important to restore the mining areas.Since vegetation restoration is the key issue of ecological restoration,this paper takes Heidaigou mining area as the study area to get an idea of how to monitor and restore the Heidaigou mining area effectively and ecologically.All the data in the research is from Landsat 5 TM which is taken from 1999 to 2011 between June and October.By analyzing the image of study area with the technologies of remote sensing and GIS,we can draw the interpreted images of different surface features from 1999 to 2011 in Heidaigou mining area.The results show that the area of exposed soil of waste\|dump is increasing in Heidaigou mining area,which means the strength of exploitation is rising.Besides,the growing area of restoration of vegetation shows that the extent and strength of restoration is also ascending year after year of Heidaigou mining area.
In recent year,ship detection of wide swath SAR images have been widely used in ocean surveillance and military reconnaissance.The background clutter property of wide swath SAR images ranges apply in different image regions due to the complex sea conditions.Two parameter CFAR detector and K-distribution\|based CFAR detector use the same distribution model which estimates the background clutter to detect the whole area.The used model is not fit for some regions,making higher loss of CFAR,bringing down the test performance.In this paper,a novel CFAR ship detection algorithm is presented which chooses the background clutter distribution model according to the multi-scale statistical variance:that is,choosing log\|normal distribution in a uniform region and K-distribution in a non\|uniform region.Then a threshold of the constant false alarm rate and the probability density function can be derived by the CFAR detector.Experimental results from 20 different wide swath SAR images are given to demonstrate that the proposed algorithm decreases the false alarms effectively and has high practical value.
Monitoring the mining geo-environment of Hongge vanadium-titanium magnetite district with IKONOS image combines with field survey data,builds evaluation index system for the experimental site with the method of fuzzy mathematics comprehensive assessment,and evaluates the mine geo\|environment of the experimental site comprehensively.After comprehensive evaluation on the five larger mine units in the experimental site ,the results show that the iron ore mines of Baicao,Panjiatian,Xiaoheijing are in the geo\|environment evaluation at Grade-Ⅰ,better than the iron ore mine of Yangquejing’s Grade\|Ⅱ and Hongge’s Grade\|Ⅲ.The research results provide a reference for evaluating the geo\|environment conditions of the mine,rational development of mineral resources,comprehensive regulation of mine ecological environment.
Terrain factor is added to correlative spectrum information partition to extract alteration information,firstly using ASTER image stereo pairs extract DEM,and classify study areas by terrain(slope),which is the sunny,semi\|sunny,semi-shade,the shade.The corresponding area is divided into four different spectral region according to its spectrum statistical information.Then using principal component analysis extract alteration information.The results show that the spectral partition alteration information extraction method can be more effective to identify the various spectral region alteration information,and reduce terrain effect on alteration information extraction results effectively,finaly,obtain very good application in the study area.
Decreasing the risk of oil spill disasters is essential for protecting the environment and reducing economic losses.Advances in remote sensing technologies can help to identify parties potentially responsible for pollution and minor spills before they caused widespread damage.Oil spill surveillance are important aspects of the application of remote sensing technology.Utilizing NASA's Earth Observing System(EOS) AMSR\|E data,combined with the threshold gradient algorithm and R\|L based on fractional differential gradient operator edge extraction algorithm,this paper proposes sea surface oil spill surveillance algorithm based on microwave radiometer.And taking the oil spilling in the Gulf of Mexico as an example,the extent of oil spill is inverted,and get a better experimental results.The two algorithm of oil spills range inversion are simple,fast computing,and the effect are obvious.They are effective complement and improve SAR,Laser fluorosensors detection instruments.
As a kind of huge environmental risk source,tailings pond could cause a huge environmental disaster to the downstream area once an accident happened.Therefore it has become one key target of the environmental regulation in China.Especially,recently environmental emergencies caused by tailings pond are growing rapidly in China,the environmental emergency management of the tailings pond has been confronting with a severe situation.However,the regulatory agency is badly weak in the environmental regulation of tailings pond by using of ground surveys and statistics which is costly,laborious and time consuming,and the lack of strong technical and information support.
Therefore,in this paper,according to the actual needs of the environmental emergency management of tailings pond,we firstly made a brief analysis of the characteristics of the tailings pond and the advantages and capability of remote sensing technology,and then proposed a comprehensive and systematic indexes system and the method of environmental risk monitoring of tailings pond based on remote sensing and GIS.The indexes system not only considers factors from the upstream area,the pond area and the downstream area in a perspective of the risk space theory,but also considers factors from risk source,risk receptor and risk control mechanism in a perspective of risk systems theory.Study show that Zhangjiakou city has up to 580 tailings pond and is nearly located upstream of the water source of Beijing,so finally we apply the proposed indexes system and method in Zhangjiakou area in China to collect environmental risk data of tailings pond in that area to work well.Through the use case in Zhajiakou,the technique of using remote sensing to monitor environmental risk of tailings pond is feasible and effective,and would contribute to the establishment of ‘Space-Ground’ monitoring network of tailings pond in future.
An object-oriented method detected land cover change,using remote sensing data spectrum information,texture features,topological relation,based on the multi\|scale segmentation of objects,establish methods for the detection of vector method and the vector similarity changes,two detection methods were successfully detected land cover change information in the experimental area.Results show that two methods of detecting changes in more than 80% in the same area of two object oriented image the same time detection of overall accuracy,but the Change Vector Method(CVA) precision is higher than that of vector similarity method.Therefore,the land cover change detection can be given priority to CVA.
In recent years,water pollution has made great crisis in Taihu Lake Watershed.Land use is seemed as an important factor for the environmental changes,so the land use classification study of Taihu Lake lakeside sensitive area has great significance.According to the local circumstance of land use and the research objectives in the study area,a novel land use/land cover classification schema is established for the Wujingang River & Zhihugang River Watershed in the upstream areas of Taihu Lake Watershed,then the reliable watershed land cover/land use information can be acquired by the means of images classification based on medium\|resolution remote sensing images.In this study,ALOS multi\|spectral remote sensing images in 2010 are used for the data sources,object\|oriented image classification are performed after the multi\|resolution image segmentation,then several classification rules based on decision tree methods are built to effectively extract the land use/ land cover information such an construction lands,road and water body.The classification results show that based on such an object\|oriented image classification method has a higher accuracy,and the overall accuracy is 88%.At the same time,the classification accuracy of the main land use types such as cropland,rural residence and urban residence are also higher.This study has some practical value to the watershed land use,and it also provides a methodological reference for land use classification of the whole Taihu Lake watershed or other plain river network regions.
The structural analysis problem of the non\|continuity geographic objects with significant spatial structures hasn't been adequately addressed.Because of the non\|linearity characteristic of structured geographic objects,the classic spatial interpolation methods such as Ordinary Kriging,which is based on Euclidean distance,can not complete this mission.To solve this issue,a concept of structured heterogeneity of the spatial data was put forward.After introducing the curve theory of differential geometry,the semivariogram model of the structured heterogeneity was analyzed,which indicates that the lag depends on the arc length between sample points and the complexity of the research region.In the next section,the analytic formulas of the lag was deduced.The above steps were followed by an approximation calculation method for experimental semivariogram of structured heterogeneity,which is able to uniformly converge to true semivariogram in the condition of the reasonable distribution of sample points.Finally,an application using the embankment settlement data of Qinghai\|Tibetan Railway was provided.
In this paper,Kraus filtering algorithm and enhanced Canny arithmetic operators were applied to extract digital elevation models by small foot\|print LiDAR point clouds;in order to enhance the urban remote sensing classification reliability and ground features extraction accuracy,the buildings and other features were extracted by objected-orientated classification method with merging multi\|sources of spatial data,through the integration of LiDAR-extracted nDSM & roughness index and CCD-derived spectral & geometrical attributes.The result demonstrated that:the estimated DEM accounted for around 96% of the variation with a standard error of 1.15m,and the two estimates were not significantly different from a 1∶1 relationship;meanwhile,the information classification method,which integrates roughness index,spectral information and shape index,could not only relief the noise of classification and reduce the classification error,but also reach a relatively high accuracy.The coefficiency of determination of the buildings areas was almost higher than 0.7,and the explanation capability of the estimated buildings heights accounted for above 92% of the variation,which showed that the result of the objected\|orientated classification by merging multi-sources was reliable for extracting 3D structure parameters of the building with a high accuracy.
Due to its capacity to describe detailed information,high spatial resolution remote sensing technology has been used increasely in forest resource dynamic monitoring,especially at large scale.Based on 0.5 m resolution Worldview\|2 data and 1∶10 000 forest subcompartment vector data,monitoring on forest resource dynamic during 2010~2011 in Pingtan Island was carried out.Object\|oriented classification method was adopted.Firstly,multi\|resolution segmentation was done by Worldview\|2 image,which produced two levels of objects.Then,hierarchical supervised classification was used to classify the second\|level objects.The objects were assigned to seven different thematic layers according to the attribute of land use types of forest subcompartment,the object samples were selected for supervised classification to identify the change of objects in each layer.Changed objects larger than 0.0667 hm2 were preserved and used for dynamics analysis.The result indicated that the overall accuracy is 90.85%,and using this method to extracting the change area is effective and feasible.
Terrestrial Laser Scanning (TLS) Technology has been widely used in digital city with the development of 3D\|laser scanner hardware and processing method of points cloud data.Nowadays,there are various kinds of terrestrial laser scanners with their distinctive characteristics which can make users confuse in practical work.Meanwhile,there are still some core problems and key techniques need to be paid much attention,especially in large building digitization and reconstruction.In this paper,two typical kinds of terrestrial laser scanners,pulse based and phase based,are studied and used in points cloud data acquiring and reconstruction experiment of large\|scale building.Key techniques of each step,such as data acquisition,data preprocessing including point cloud registration and noise filter,building modeling including plane segmentation and reconstruction,are analyzed and explored.Solutions are proposed for some existing problems which need to be studied further.
Digital Down Converter(DDC) is an important part of the radar-receiver,and its function is to convert the frequency of the digital signal received to base\|band.In this paper,main function is to complete the quadrature demodulation,filtering and the down sampling.The Actel FPGA chip based on Flash is chosen as the security and the resource concern.A new method based on pipeline technology,simplified quadrature demodulation and distributed filter arithmetic is used to implement the DDC module.
Low-resolution remote sensing data are kinds of very important data source to global change research,which is with of high temporal resolution and large coverage.However,multi\|source low\|resolution data have their own characteristics,such as geographical coverage,data accumulated time,which could not satisfy the demands of global change research by single low\|resolution data.Therefore,it is necessary to combine multi\|source large\|scale low resolution remote sensing data together being mutually complementary to meet its requirement.It is very necessary to make the multi\|source remote sensing data be consistent with geo\|location firstly,so a series of analysis and experiments of geometric correction was carried out.MOD09A1,as a kind of standard data product with higher accuracy of geo\|location,is used as the base data.Relative geometric accuracy evaluation between the base data and L1B data of NOAA/AVHRR,FY\|3/VIRR,FY\|3/MERSI,FY\|2/VISSR was done respectively.The result shows that the difference in geo\|location between the base data and the L1B data mentioned is significant,which is not good for the combination of them.Meanwhile,multi\|order polynomial geometric correction based on sparse and evenly distributed Ground Control Points(GCPs) of the data mentioned was carried out,since the selection of GCPs on low spatial resolution data was difficult.The result indicates that low\|order polynomial geometric correction could make a remarkable improvement on geometric accuracy of the multi\|source remote sensing data and be consistent in geo\|location with the MODIS base data,which would meet the requirement of the combination of multi\|source remote sensing data in global change research.
Vegetation is an important part in ecological system and indicating certain landscapes,It is a meaningful work to obtain detailed information of vegetation using GeoEye image with its abundant spatial and spectral information.This study fused the panchromatic band and multispectral bands with four image fusion methods,Image segmentation is the first and critical procedure in the workflow of object\|oriented image analysis,discrepancy between reference polygons and corresponding segment is used to assess segmentation quality in this study.We extracted the vegetation information using classification feature which is selected from the perspective of remote sensing image cognition and geographical understanding.The results showed that Gram\|Schmidt(GS)method is the most effective in fusing panchromatic bands and multispectral bands,And object\|oriented classification is effective in high resolution remote sensing information extraction,the overall accuracy is up to 90.3%.this research provided an effective method for vegetation information extraction.
Assisted with object\|oriented technology,shadow area extraction and internal compensation are processed.On the basis of multi\|solution segmentation,spectral and geometric characteristics are fully utilized in object feature function setting and shadow area extraction in this paper.Furthermore,using mathematical morphology,edges of shadow region are generalized according to spatial relationship and internal information can be recovered though gray\|scale histogram mapping strategy.Finally,along the shadow boundaries,median filter are processed to optimize buildings shadow extraction results.
The parametric model of sea state bias can be divided into crossover model and collinear model according to the dataset that used,both of the two kinds of models have their own advantages and disadvantages.Fusing the crossover dataset and collinear dataset to establish fusion dataset,comparing to the fusion dataset,crossover dataset and collinear dataset,the results show that the fusion dataset has advantage in temporal,space coverage and data value.Its spatial resolution meets the satellite radar altimeter observations demand.The explain variance of the model which computed from fusion dataset is higher than the others,which means the model is more efficiency.
