最新刊期
卷 20 ， 期 2 ， 2016
- 摘要：The classification of remote sensing data plays an important role in all stages of remote sensing data processing and analysis.With the increase in the volume of remote sensing data, new problems concerning remote sensing big data classification tasks arise. Currently, the commonly used classifiers are usually designed for simple tasks to provide satisfactory results. However, for the processing of large volumes of remote sensing data, the scalability of classification efficiency and precision should be further investigated. Therefore, this study emphasizes on the comparisons of the scalability of typical remote sensing data classification methods to achieve this goal. Method:This study takes remote sensing image scene classification as an example and selects four well-known classification methods for comparison, namely, K Nearest Neighbor（KNN）, Random Forest（RF）, Support Vector Machine（SVM）, and Sparse Representation-based Classifier（SRC）, to conduct scalability analysis. The comparisons are conducted in terms of parameter sensitivity, effect of training sample data volume on classifier performance, effect of testing sample data volume on classifier performance, and effect of feature dimension on classifier performance. Results: The experimental results are as below:（1） The classifiers of KNN, RF, and L0-SRC are less parameter-sensitive than the classifiers of RBF-SVM, Linear-SVM, and L1-SRC.（2） In cases where the samples to be classified are fixed, all the classifiers tend to increase with the increase in the number of training samples. The SRC-type classification methods have the highest accuracy, followed by the SVM-type classification methods, the RF, and the KNN classifiers. In terms of overall classification time, the results show that the methods can be ranked as below: L0-SRC > L1-SRC > RF > RBF-SVM/Linear-SVM > KNN/L0-SRC-Batch.（3） In cases where the training samples are fixed, the classification accuracies of all the classifiers are seldom affected by the number of samples to be classified, which may be due to the learning abilities of all the different classifiers.（4） The feature dimension affects the efficiency and accuracy of different classifiers, in which SRC and KNN can obtain satisfactory results without high feature dimensions. SVM is tolerant to high feature dimensions and has a good learning ability with such high feature dimensions. By contrast, RF is insensitive to the increase in feature dimensions, and higher feature dimensions do not contribute much to the improvement of classification performance. Under such circumstances, the RBFSVM exhibits the best performance, followed by the L1-SRC classifier, the Linear–SVM classifier, and the RF and L0-SRC/L0-SRC-Batch classifiers. In terms of overall classification time, the classifiers of L1-SRC and L0-SRC are the most time-consuming, whereas the other classifiers have relatively higher efficiency. Conclusion: Different classification methods have different advantages and disadvantages. In the tasks of classifying a large volume of remote sensing data, the selection of classifiers should be balanced and based on their characteristics and practical applications. Generally, a classifier that is less parameter-sensitive and less time-consuming during classification and obtains more accurate classification results is preferable.关键词：remote sensing image;data classification;scene classification;classifier;scalability
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发布时间：2021-06-10 - 摘要：The factors that influence China’s wetlands are greatly complicated because of global climate changes and rapid economic development. This study investigates the dynamic characteristics and evolution laws of the temporal and spatial distribution of wetlands in China,as well as the driving forces behind these changes. Considering the relevance with wetland change and data availability, we chose 12 impact factors as independent variables（average temperature, average humidity, accumulative precipitation, population, gross regional domestic product, agricultural production value, agricultural acreage, grain production, effective irrigation area, reservoir capacity, drainage area, and saline-alkali management area）, in which three were natural factors and nine were social economic factors. The wetland change driving mechanism from 1978 to 2008 was studied using Geographically Weighted Regression（GWR） based on the wetland remote sense mapping in four years（1978, 1990, 2000, and 2008） and land use data in three years（1990, 2000, and 2005）. GWR is a local linear regression method that can effectively reflect the regional disparity of driving factors influencing wetlands and can present intuitive results. The main influencing factors of different types of wetlands vary. Inland wetlands were closely associated with average temperature, accumulative precipitation, and activities related to farming irrigation, whereas economic development and water infrastructure significantly influenced artificial wetlands. Coastal wetlands were closely associated with population and fishery industry. The main factors influencing a wetland changed with time, and obvious differences in the degree of influence over the space were observed. For inland wetlands, accumulative precipitation affected the northwest arid region from 1978 to 1990. The average temperature significantly positively correlated with inland wetlands in the north areas, where snow and permafrost were distributed from 1990 to 2000. Both of them can increase the wetland water supply to expand the wetlands area. The drainage areas on inland wetlands significantly influenced the southeast coastal area. Agricultural acreage, effective irrigation, and grain production significantly influenced the north, especially in three northeast provinces and the Inner Mongolia autonomous region. Due to these factors, inland wetlands sharply reduced because of drainage, reclamation, and increasing agricultural demand for water. Artificial wetlands are consistent with changes in economic development in China from 1978 to 2008. During this period, economic development moved from south to north and from east to west, and artificial wetlands increased accordingly in those areas. In the past 30 years, the reduction of coastal wetlands was mainly caused by fisheries development, tideland reclamation, oilfield development, infrastructure, and water conservancy facility construction. Among these factors, fishery production mainly affected Jiangsu and Zhejiang provinces,tidal land reclamation affected Fujian and Guangdong provinces, and oil field development significantly affected the areas around the Bohai Sea. At the same time, the population growth rate was faster in coastal areas than in other regions, resulting in the conversion of wetlands into a large number of artificial facilities.The results of this study basically reflect the characteristic changes in China’s wetlands from 1978 to2008, which could provide helpful policy support for the management and rational utilization of wetlands on a national scale.关键词：wetlands change;driving force;China;geographically weighted regression;remote sensing
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发布时间：2021-06-10 - 摘要：Currently existing quantitative remote sensing products are mainly based on source data from the same sensor. However, the use of a single data source limits the space–time continuity and accuracy of these products. In recent years, more sensors have become available,and an increasing demand to employ multisource data emerges to generate quantitative remote sensing products. We build a multisource data synergized production system to materialize this idea. However, data from different sensors are quite heterogeneous in many aspects, such as file naming rule, data format, number of files, geometric positioning, and band setting. The heterogeneity of multisource data has many challenges in almost all parts of the production system in the process of system development and further extension. Developers have implemented and exhausted diverse logics to tackle the difference among multisource data. Therefore, we propose a set of domain models for the data from different sensors to share the same behavior when addressing, moving, and dispatching. The domain model should be sufficiently flexible to adapt new data in arbitrary form when new products are integrated in the future. Based on the unified behavior of data, all related functions in the system are also unified and can be freely used regardless of the data type. Domain-driven design is a software design philosophy that tackles core complexity in the heart of software by iteratively refining a set of core domain models. A refined domain model is valuable because of its high reusability. Domain models can ensure that all business logics have unified interfaces throughout a large system.We propose a set of domain models for each business scenarios in our system, such as data hierarchy, filename parsing, repository pattern,script building, order configuration, and job running. Based on the proposed domain model, we have built a comprehensive multisource data synergized quantitative remote sensing production system. The system is order-driven and can automatically produce the required products.Cascade production is also supported, which means that, if a high-level product requires nonexistent low-level products, then these low-level products will be spontaneously produced. The function modules include data import, order management, data search, and production. The system supports approximately 30 types of source data from almost all commonly used sensors and more than 40 types of quantitative remote sensing products. Thousands of products have been generated, and the system performs well. During system development, the number of code lines and function points are significantly reduced by using the proposed domain model. During system extension, the domain model adapts well and is completely compatible with all new types of data and products without exception and modification. The proposed domain model has shown its generality to generate multisource data with unified behavior and flexibility to adapt new data and products. The model can significantly reduce the number of code lines and function points. Thus, system development and extension have become easier and more effective. The domain model can also be referenced by other data synergized production systems. In the future, we will attempt to refine the domain model to make it more versatile and flexible.
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发布时间：2021-06-10 - 摘要：Remote sensing data lack a unified standard format, whereas multisource remote sensing products involve a variety of file formats based on the study of remote sensing data formats and the corresponding libraries GDAL, HDF4, HDF5, and NOAA AVHRR 1B format standard from China Meteorological Administration as reference. With the use of a factory model, a multisource remote sensing data format with a unified user interface is designed and implemented to obtain a better data format conversion tool between the Geo TIFF and HDF5 formats. Based on the study of various remote sensing image data file formats, the factory mode can not only shield the user from instantiating the details of different classes but also implement different types of operations to achieve a unified operational view, which is user-friendly. Given the lack of existing remote sensing data format libraries, GDAL, HDF5, and any format library cannot be used to read and write alien data formats consistently. Instead, the factory model is used, different remote sensing data formats are abstracted, and a new top-level architecture DFAL library is designed to solve the problem of reading and writing multisource remote sensing data formats. The multisource remote sensing data abstraction libraries DFAL are integrated into the underlying data IO for the quantitative production of a multisource remote sensing system. More than one multisource quantitative remote sensing production system is tested in the multitask parallel platform. The DFAL library causes no errors, and the usability and usefulness of the DFAL library have been proven. The DFAL library significantly simplifies the algorithm program for multisource quantitative remote sensing production, such as the production of multisource quantitative products mainly involving three different data formats that can be consistently read and written through the DFAL library and the IO code. Only one third of the logical workload is not used in the DFAL library. The multisource remote sensing data format abstraction library DFAL has generated a unified interface. Thus, remote sensing professionals can be liberated of the complex remote sensing data formats and can devote more energy to the study of the ontology of remote sensing. Future work can focus on three aspects of research and expansion, as below:（1） the remote sensing data formats are extended to ensure efficient processing by DFAL, and automatic formats for matching remote sensing data are achieved;（2） the projection transformation between different projections based on the DFAL library is supported, and the cooperative processing of multisource and multi projection data is applied;（3）the read and write operations are supported for multisource remote sensing time series data, which better serve the multisource and long time series applications of remote sensing.关键词：geospatial data abstraction library;hierarchical data format 5;integration of remote sensing data formats;data format abstraction library
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发布时间：2021-06-10 - 摘要：Aerosol Optical Depth（AOD） is a key factor that reflects the impact of atmospheric aerosol on the climate. The accurate simulation of AOD is the basis for a climatic model in modeling the aerosol climate effects. In this study, an online-coupled three-dimensional regional climate–atmospheric chemistry–aerosol model was used to investigate the AOD over eastern China in 2010 to understand the spatial and seasonal behavior of AOD and the role aerosol plays over this region.The Regional Integrated Environmental Modeling System online-coupled with atmospheric chemistry and aerosol processes（RIEMS-Chem） was used in this study.This model contains sophisticated atmospheric dynamic, atmospheric chemical, and aerosol chemical processes. The onlinecoupled system facilitates the model to simulate the impact of aerosol on climatic factors and the feedback of climate on aerosol distribution.Simulated AOD was verified by satellite retrieval of the Moderate-Resolution Imaging Spectroradiometer（MODIS） and insitu measurements from the Aerosol Robotic Network（AERONET） in four seasons.The comparison of the AOD simulation result with the corresponding MODIS retrieval indicated the capability of the model to reproduce the seasonal variation and spatial distribution of AOD over eastern China, although the model somewhat underestimated the magnitude in summer.The comparison of AOD measurements from the six AERONET sites also showed that the model was able to simulate the spatial and seasonal variation of AOD, but underestimated the magnitude over north China, with an overall correlation coefficient of 0.6 compared with all AERONET measurements. The MODIS retrieval and corresponding simulation result showed that, over eastern China, the AOD was generally higher in summer,followed by spring, and lower in autumn and winter. However, the daytime seasonal mean AOD showed different seasonal distribution patterns:For areas around the North China Plain, the daytime seasonal mean AOD reached its highest level in summer with values ranging from 1.1 to 1.5, but lowest in other seasons. By contrast, over areas of the middle and lower reaches of the Yangtze River, the daytime seasonal mean AOD was reached its highest level in spring with values ranging from 1.1 to 1.7, followed by autumn and winter, but lowest in summer. For the region of eastern China,the daytime seasonal mean AOD reach edits highest level over areas north of the Yangtze River in summer, over areas south of the Yangtze River in winter, and over areas along the middle and lower reaches of the Yangtze River in spring and autumn.Model verification indicated that RIEMS-Chem was able to reflect the characteristics of AOD distribution and its seasonal variation, but under predicted the observation by approximately 20% in terms of the annual mean. According to the model result, the daytime AOD distribution exhibited distinct subregional and seasonal characteristics over eastern China, implying that, over this region, aerosol optical and climatic effects could be subregional and seasonal dependent.关键词：regional online-coupled model;Aerosol Optical Depth（AOD）;seasonal variation;eastern China;MODIS;AERONET
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发布时间：2021-06-10 - 摘要：GF-1 PMS data have an important role in land cover detection and quantitative information extraction because of its high spatial resolution and short revisit period. Land surface reflectance calculated by using atmospheric correction is a key step in the application of GF-1 PMS data in land surface parameter detection. However, atmospheric correction is relatively limited because of the lack of a shortwave infrared near 2.1μm, which is necessary for Aerosol Optical Depth（AOD） retrieval that requires correction. This study aims at developing a method to retrieve the AOD and obtain high-precision land surface reflectance from atmospheric correction.A new algorithm for AOD retrieval from GF-1 PMS data was developed by introducing a prior surface reflectance data set, and the retrieved AOD was applied in atmospheric correction. In this method, Moderate-Resolution Imaging Spectro-radiometer surface reflectance outputs（MOD09） were used to obtain the land parameters required in AOD retrieval with Dense Dark Vegetation（DDV） method, with which the AOD was obtained from GF-1 PMS data. The land parameter obtained from the surface reflectance data set was the Normalized Difference Vegetation Index（NDVI）, which was used to determine the pixel distribution of DDV. GF-1 PMS data were processed to match the selected MOD09 data in spatial resolution. The percentage matching strategy of regional NDVI distribution was adopted to reduce the effect of registration errors produced from pixel-by-pixel matching of the two types of data.Aerosol Robotic Network ground measurements AOD in the Beijing and Taihu stations have been used to evaluate the accuracy of the derived AOD from GF-1 PMS. Results show that AOD retrieval with this method can obtain a high precision. The accuracy of land surface reflectance obtained from atmospheric correction of GF-1 PMS has been estimated by using the surface reflectance of typical types measured in the Beijing and Dunhuang areas. The uncertainty of land surface reflectance for different land covers is less than 0.015.This work has improved the application level of GF-1 satellite, particularly its quantitative application, by developing a method to obtain the AOD and land surface reflectance with high precision and provide an example for the processing of the same type of satellite data.The method can also be used to achieve AOD inversion and atmospheric correction of other terrestrial observation satellite data.关键词：GF-1 PMS;surface reflectance data set;aerosol optical depth;atmospheric correction
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发布时间：2021-06-10 -
Change of Landsat 8 TIRS calibration parameters and its effect on land surface temperature retrieval
摘要：The thermal infrared data of the Landsat series satellite are the main data for the monitoring of the Earth’s surface temperature change. The Thermal Infrared Sensor（TIRS） of Landsat 8, a new generation of Landsat series satellites, contributes to this important mission. The TIRS data from Landsat 8 have been widely utilized since its launch on February 11, 2013. Several algorithms for the derivation of land surface temperature（LST） from TIRS data have also been developed successively. Studies on TIRS-data calibration accuracy and the precision of related LST inversion algorithms have been conducted. This paper presents a brief summary of the changes in TIRS-data calibration parameters and analyzes the suitability and effectiveness of current LST inversion algorithms based on the time point between current calibration parameters and LST inversion algorithms. In particular, the paper focuses on the suitability of two recently proposed split-window algorithms for current TIRS data. Users and researchers can therefore acquire further understanding of the algorithms and properly apply them to Landsat 8 TIRS data. In general, owing to the influence of significant stray light coming from outside TIRS’ field of view, the calibration accuracy of TIRS data still cannot meet design goals, and the uncertainty of TIRS band 11 is twice as large as that of TIRS band10 at this stage. Moreover, the root mean square error of both TIRS band 10 and band 11 is much higher than that of Landsat 7 Enhanced Thematic Mapper Plus（ETM+） band 6. Therefore, before the problem of out-of-field stray light can be completely resolved, employing a split-window algorithm to retrieve LST using both TIRS band 10 and band 11 should be avoided as this may cause a large amount of uncertainty in the results. Users should work with TIRS band 10 data as a single spectral band, similar to Landsat 5 Thematic Mapper（TM） band6 or Landsat 7 ETM+ band 6, given the large amount of uncertainty in the band 11 values. Users may use the single-channel（SC） algorithm of Jiménez–Mu?oz and Sobrino to retrieve LST using TIRS band 10 when the atmospheric water vapor content is less than 3 g/cm2.关键词：Landsat 8;thermal infrared data;calibration;split-window algorithm;land surface temperature retrieval;cross comparison- 290
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发布时间：2021-06-10 - 摘要：Studies on hyperspectral remote sensing image classification have developed rapidly with the progress of related disciplines, including pattern recognition, machine learning, and remote sensing technology. This review generates a systematic summary and conducts a comprehensive evaluation of the advancements in current techniques for hyperspectral remote sensing image classification. Based on an overview of different classification schemes, we examine the recent progress in per-pixel classification algorithms for hyperspectral images from six aspects, namely, new classifier design（e.g., kernel-based methods）, feature mining, spectral spatial classification, active and semisupervised learning, sparse representation for classification, and multiple classifier systems. Future research directions are discussed as well.On the one hand, new theories and methods of machine learning should be introduced continuously into hyperspectral image classification.Moreover, multisource data and multidimensional feature spaces may improve the accuracy, generalization capability, and automation degree of a classifier. On the other hand, new classification methods should be designed in consideration of practical requirements to meet the needs of real applications and to emphasize the advantages of fine spectra in hyperspectral remote sensing.关键词：hyperspectral remote sensing;classification;support vector machine;feature mining;multiple classifier system
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发布时间：2021-06-10 - 摘要：This review surveys the most currently used evapotranspiration estimation methods based on remote sensing. The methods are divided into different categories, including surface energy balance models, Penman–Monteith models, land surface temperature–vegetation index space methods, Priestley–Taylor models, empirical statistical methods, complementary methods, and land process assimilation models. These methods are mainly focused on two problems. The first problem is how to deal with aerodynamic surface temperature used in gradient diffusion equation. One-source models have two major solutions. One is to use remotely sensed radiative temperature as a direct substitution for aerodynamic surface temperature or use semi-empirical relations to obtain the surface–atmospheric differential temperature.In two-source models, the heat fluxes are decomposed by component temperature. The second problem is how to calculate surface resistance that influences heat flux exchange from the soil surface and canopy layers. This process is complicated to calculate at the local scale.Basing from the two problems, scientists combined the features and superiority of remote sensing, such as the relationship between various vegetation indices and surface parameters with the fundamental theory of these methods, and developed these methods from saturated surface toward unsaturated surface to calculate evapotranspiration.However, severe scale effects emerge when these methods are used in heterogeneous surfaces. Surface heterogeneity influences the driving force of evapotranspiration estimation. Thus, three methods to correct scale error over heterogeneous surfaces are shown in detail: area weighting method, correction factor method, and land surface temperature downscaling（or thermal spatial sharpening） method. The core concepts of these methods are to couple high and low spatial resolution satellite data and statistically quantify the inhomogeneity in mixed pixels to correct the scale error in evapotranspiration estimation. In meteorological science, land process models using mosaic（or areaweighted method） and statistical dynamic methods are developed for heterogeneous surfaces. In the statistical dynamic method, one single or two mutually independent surface parameters or atmospheric forces are regarded as random variables. Consequently, their spatial distributions are expressed as probability density function to be melted into evapotranspiration retrieval models. These ideas are worthy to be adopted in remotely sensed evapotranspiration estimation. As another important part of evapotranspiration estimation, the progress of turbulent heat flux validation research over heterogeneous surfaces is introduced at length from the angle of observation experiments. The measured heat fluxes possibly originate from source areas instead of in situ pixels. Thus, the traditional validation method that compares the retrieved pixel value with the observation value to evaluate result accuracy may cause uncertainty. Some schemes are developed using footprint models to determine the scope and weight of source area pixels and decrease the uncertainty in validation. Moreover, we provide a brief introduction of the basic principles of the footprint models that are mostly used in remote sensing in the present case. These models include Eulerian analytic flux footprint models and Lagrangian stochastic trajectory approach. Finally, we discuss the challenges that researchers may encounter in the future to develop representative models of remotely sensed evapotranspiration retrieval over heterogeneous surfaces.关键词：remote sensing;evapotranspiration;Model;scale effect;heterogeneous surface;surface observation experiment
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发布时间：2021-06-10 - 摘要：The lunar irradiance reflected from the sun is a benchmark for radiance calibration in the visible and near-infrared spectra. Lunar calibration has become a new method to calibrate and validate satellite-based instruments. The Moderate Resolution Spectral Imager（MERSI） on FY-3C adds a function to monitor the lunar disk to improve the on-orbit calibration of solar reflective bands（SRB） for the MERSI. In this study, 11 groups of the dataset have been collected to record the lunar measurements since the launch of the FY-3C. With the ratio between the target channel and the referred channel, the effects of the lunar phase angle and the relative distance of the solar–lunar satellite have been removed from the measured lunar irradiance. The SRB of the MERSI in the visible and near-infrared spectra have been calibrated from the lunar measurements. The long-term trend of the SRB of the MERSI has been analyzed. The annual attenuation rate has increased to14.55% and 8.42% for bands 8 and 9, respectively. The annual attenuation rate is between 1.15% and 4.72% for bands 1, 6, 10, 11, 16, and19. No attenuation is observed for the rest of the bands. The aforementioned results can be used to correct the systemic bias of the radiance calibration of the MERSI. The accuracy of the radiance calibration of the MERSI can be improved on basis of the lifetime of the instrument.关键词：lunar calibration;FY-3C/MERSI;visible and near-infrared spectra;radiance response stability;lunar irradiance
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发布时间：2021-06-10 - 摘要：The Vegetation Index method is often used in remote sensing estimation of vegetation Fraction of Absorbed Photosynthetically Active Radiation（FAPAR）, but the estimation accuracy of the Vegetation Index method is often affected by "saturation" of spectral reflectance in the"red band absorption peak." The article is aimed at developing new parameters to improve the estimation precision of FAPAR,particularly in average or high vegetation coverage. Given that the hyperspectral absorption characteristic parameters can be used to interpret the spectral absorption feature details of ground object, we develop the automatic recognition method of the characteristic absorption peakof the hyperspectral curve based on the differential and envelope removal methods to identify thecharacteristic absorption peaks sensitive to FAPAR. We extract the hyperspectral absorption characteristic parameters based on the continuum removal method and Spectral Absorption Index（SAI）. By combining the characteristic absorption peaks with hyperspectral absorption characteristic parameters, we build a hyperspectral absorption characteristic parameters model to estimate the FAPAR of natural grassland canopies. The results are listed below:（1） The hyperspectral absorption characteristic parameters have a high correlation with the FAPAR, and the SAI of the "red band absorption peak" is the most sensitive parameter to the change of FAPAR. Compared with the reflectivity of the "red band absorption peak" and Normalized Difference Vegetation Index（NDVI）, saturation in the high vegetation coverage level was significantly improved.（2） The best estimation model is the logarithmic equation, which takes the SAI of the "red band absorption peak" as a variable. Compared with the NDVI model, the prediction accuracy of FAPAR exhibits varying degrees of improvement while in the average or high vegetation coverage level. The hyperspectral absorption characteristic parameters can remedy, to some extent,the defects caused by the"saturation" problem of the Vegetation Index in estimating FAPAR, can be used as a new inversion parameter of vegetation FAPAR, and can monitor the FAPAR of natural grassland in the average or high vegetation coverage level.关键词：hyperspectral curve;characteristic absorption peak;absorption characteristic parameters;fraction of absorbed photosynthetically active radiation;natural grassland
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发布时间：2021-06-10 - 摘要：Man-made target extraction from space-borne polarimetric synthetic aperture radar（SAR） imagery has been extensively investigated. However, challenging issues caused by polarization orientation angle（POA） shifts and various complex backscattering signatures still remain. Utilizing quad-polarimetric SAR（Quad-Pol SAR） imagery for classification and target detection in urban areas has three major issues:（1） POA shifts;（2） Complex backscattering diversity;（3） Close similarity between targets of weak backscattering signals.This study proposes a novel approach to extract urban synthetic targets from Quad-Pol SAR imagery with roll-invariant parameters to overcome the three aforementioned issues.This study used the dataset of Quad-Pol SAR imagery acquired by Radarsat-2 to extract the synthetic targets in the urban area. Landsat 8 imagery was used as a reference for comparison.The case study area is located in Nanjing City, Jiangsu Province, People’s Republic of China.In the first step of the proposed method, the target scattering vector model, which supplied more parameters than the previously proposed models,was applied to generate the roll-invariant parameters. Subsequently, the relief algorithm suitable for binary classification was introduced to optimize polarization parameters. Considering feature selection and scattering mechanism analysis, two parameters, namely,scattering-type magnitude（parameter αs） and target helicity（parameter τm）, are adopted. Finally, support vector data description was employed to identify synthetic targets because of its efficiency and robustness. In addition to αs and τm, morphological profiles were employed to improve the extracted result.Result shows that the roll-invariant parameters αs1（the domain scattering-type magnitude） and τm2（the secondary scattering helicity of target） are effective for extracting the urban built-up area. Cropland, vegetation, and bare soil can be easily distinguished from the synthetic targets by integrating the parameters τm2 and αs1.The biophysical composition index（BCI） and normalized difference built-up index（NDBI）are derived from optical images. However, these indices confuse bare soil and synthetic targets. The proposed method can well recognize more synthetic targets,such as squares, streets, and other weak backscattering targets than typical decomposition like the Yamaguchi four-component decomposition. The parameters of H/A/alpha decomposition have the roll-invariance property. However,the entropy of urban areas and vegetation are highly similar.Compared with the traditional method of POA compensation applied in the Yamaguchi four-component decomposition, the roll-invariant parameters are more effective in solving the problem of the POA shift.The introduced decomposition model supplies more parameters than H/A/alpha decomposition, which leads to higher accuracy in extraction. The selected parameters αs1（scattering angle characteristics） and τm2（symmetry polarization characteristics） of synthetic targets homogeneously distributed in space, which prevents the built-up area from being segmented into several classes and improves the efficiency of the extraction algorithm. Furthermore, the extracted result shows that the proposed method has significant advantages in distinguishing bare soil and urban targets compared with the results of optical indices, such as BCI and NDBI. The analysis of the scattering mechanism and experimental results reveal the effectiveness of the roll-invariant parameters.
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发布时间：2021-06-10 - 摘要：Sea Surface Temperature（SST） is one of the important parameters of hydrodynamic and oceanographic research. It has been widely utilized in the study of climate change, weather forecasting, numerical weather prediction（NWP）, atmospheric and oceanographic applications, fisheries, and other sciences. This study compares SST derived from Wind Sat to those directly observed by NDBC/TAO buoy and SST provided by AMSR-E. In the collocation of Wind Sat and buoy data, an SST value with a rain rate larger than zero was rejected. Collocation adopted a ±30 min time constraint and 25 km maximum separation. A total of 82,247 Wind Sat-NDBC collocations and 366,693 WindSat-TAO collocations were acquired. Rsults show that Wind Sat SST has a mean bias of 0.10 °C and standard deviation of 0.59 °C in the coastal and offshore waters of the United States. In the tropical Pacific Ocean, Wind Sat SST has a mean bias of –0.15 °C and standard deviation of 0.33 °C. In addition, Wind Sat SST has an increasing warm bias or a decreasing cool bias in summer. The standard deviations of the satellite-derived SST are both relatively large in the US East Coast and in the Gulf of Mexico; the standard deviations of certain regions are higher than 1 °C. When the buoy wind speed ranges from 5 m/s to 10 m/s, the accuracy of Wind Sat SST is good, and the mean bias and standard deviation are relatively constant. When the buoy wind speed exceeds 12m/s, the uncertainty of Wind Sat SST increases. Global comparison with AMSR-E shows that Wind Sat monthly averaged SST is cooler than that of AMSR-Ein summer and warmer in winter. Even though it has operated beyond its designed life of seven years, Wind Sat SST continues to exhibit acceptable accuracy. Wind Sat retrieves SST better in tropical Pacific and worse in US coasts and in the Gulf of Mexico. The part of wind direction correction in retrieval algorithms needs to be improved to increase the accuracy of SST retrieval under conditions of high wind speed.关键词：sea surface temperature;Wind Sat;NDBC buoy;TAO buoy;AMSR-E
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发布时间：2021-06-10 - 摘要：The change detection method is extensively used in the extraction of paddy rice using remote sensing images. However, the precision of paddy rice classification is reduced by "cloud contamination" and "salt and pepper." "Cloud contamination" occurs frequently in paddy rice planting areas during the autumn season. The images obtained by using the change detection method to extract paddy information lead to missing spectral information and constraints in using change detection. "Salt and pepper" occurs by misregistration, and a variety of errors are encountered in the classification, which yields false information and results in the accumulation of deviation during extraction. However, these two crucial issues have a detrimental effect on the ability to extract paddy accurately, which must be solved to increase the accuracy of extraction. In this study, an innovative model called temporal–spatial–fusion model（TSFM） is proposed to reduce the effect of these noises.In this model, we built a temporal–spatial–belonging degree algorithm. First, the TSFM calculated the attribution probability of the target pixel by using the spectral information of neighborhood pixels in spatial dimensions. We searched the classification of each thematic map in a critical period of paddy growing with a window and calculated the belonging degree in spatial dimensions. Second, we computed the mean of the belonging degree of pixels in the same geospatial location by using the time series of remote sensing images in time dimensions, which is the temporal–spatial–belonging degree of the pixel. Third, the paddy rice was extracted by defining the threshold derived by using the change magnitude threshold determination method.Post-classification comparison（PCC） and majority voting（MV） were introduced to map the paddy rice and to validate the proposed algorithm. We assessed the precision of the result of paddy rice in the entire study area and the "cloud contamination" area with confusion matrix method. The degree of landscape fragmentation was used to assess the effect of the mixture of spectral information of crop, which should be analyzed. Thus, two districts were selected as study areas with different degrees of landscape fragmentation based on a visual appraisal of the study area. The accuracy was compared, and the applicability and difference using TSFM were analyzed in these two regions.Experimental results show that the precision of the user, accuracy of the producer, and overall accuracy of the TSFM with 3 × 3 window size are 93.4%, 83.5%, and 87.9%,respectively. Compared with the traditional change detection method of PCC, these precisions are higher than 2.3%, 12.3%, and 9.3%. When different window sizes are used to identify the paddy rice, these precisions are higher than that of the PCC results. The overall accuracy is better than 92.0%,and the omission errors of the PCC and MV are reduced by 14.0% and 7.6%, respectively, in the area of cloud contamination. The results of classification using TSFM with different window sizes in the regular and fragmented regions varied, providing a foundation for the use of TSFM in different landscapes.Experimental results showed that the TSFM effectively solved the problem of errors from "cloud contamination" and "salt and pepper."The TSFM provides a new and potentially effective method for paddy rice mapping based on change detection. In the future, we will attempt to apply this method to a large area in China with fragmented and complex landscape.关键词：identification;temporal-spatial-belonging degree;change detection;landscape features;classification accuracy;paddy rice
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发布时间：2021-06-10 - 摘要：Ship detection is important in military and civilian applications. Synthetic Aperture Radar（SAR） with all-day, all-weather, and ultra-long-range characteristics has been extensively used. The two-parameter Constant False Alarm Rate（CFAR） method is one of the most well-known methods for target detection. CFAR is an adaptive threshold detection scheme that works efficiently when the background clutter is unevenly distributed. However, in recent years, the resolution of SAR images is significantly improved by the rapid development of the SAR sensor. With the improvement of the resolution, the size of SAR images significantly increased and the ship targets no longer appear as point targets. Instead, the ship targets appear as hard targets. The contour of the targets becomes clearer as well. When the two-parameter CFAR is used to detect ship targets with good contour, a longer computation time is needed. Message Passing Interface（MPI） parallelization is a workable solution used to shorten the computation time of two-parameter CFAR with MPI parallel technique.The traditional MPI parallelization divides the SAR image horizontally/vertically on average. However, in practical applications, preprocessing methods, such as land masking and geometric correction, are conducted before detection. These preprocessing methods can cause the uneven distribution of the points to be detected. This uneven distribution leads to the unbalanced tasks between the parallel processes.Thus, the efficiency of MPI parallelization is highly influenced. The objective of this study is to eliminate the negative influence caused by the uneven distribution.In this study, we propose an improved MPI parallel solution of the two-parameter CFAR ship detection method, in which the SAR image is divided in terms of the number of points to be detected. The partitioning strategy includes: First, the total number of points to be detected is calculated. Second, the approximate number of responsible points for each process is computed. Third, the responsible rows of image for each process are identified.In this manner, the entire detection task is equally divided among the processes. The details of the improved parallel algorithm are listed as below:（1） The first process computes the partitioning strategy and transmits it to the other processes.（2）Each process imports its responsible part of the image.（3）Each process implements two-parameter CFAR detection on its responsible part of the image.（4）The first process obtains the detection results from the other processes.The numerical experiment is conducted on a cluster computer. When the number of processes is 8, the speedup of the improved parallel algorithm is 7.45, which is better than that of the normal parallel algorithm. The efficiency of parallelization increases by approximately43%. A similar experiment is conducted on a multicore computer, and a similar result is obtained.The experimental results show that the improved parallel solution can shorten the detection time and improve the parallel efficiency of the cluster or multicore computer. This study is positively significant for real-time ship detection based on airborne SAR images. Further research is needed to shorten the detection time by using the GPU or Intel MIC architecture.关键词：ship detection;Constant False Alarm Rate（CFAR）;SAR images;Message Passing Interface（MPI） parallelization;size of detection window
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发布时间：2021-06-10 - 摘要：<正>"第二十届中国遥感大会"拟定于2016年8月在深圳市召开。会议由中国遥感委员会、深圳市人民政府主办,中科遥感科技集团、深圳大学承办。本届会议旨在交流近年来国内遥感领域在理论、技术与应用等方面的最新进展,展示遥感技术最新成果。会议同期将举行"第九届中国青年遥感辩论会"、新技术新成果展览会及项目洽谈会等。
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发布时间：2021-06-10 - 摘要：<正>封面图片为北京二号遥感卫星星座3号2016年1月25日（侧摆:16°）获取的阿拉伯联合酋长国阿布扎比亚米级分辨率真彩色融合图像。该星座由二十一世纪空间技术应用股份有限公司投资建设,是中国航天领域核准的首个商业遥感卫星星座,现已被纳入国家民用空间基础设施规划。北京二号遥感卫星星座由3颗24 km宽刈幅、亚米级高分辨率的光学卫星组成,于2015年7月11日成功发射,3颗卫星在同一太阳同步轨道面上间隔120°等相位分布,右阵九35°的侧视能力可实现全球任意目标每日重复观测,提供覆盖全球、时间分辨率和空间分辨率俱佳的卫星遥感数据和空间信息产品,可
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发布时间：2021-06-10
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