Region-based classification by combining MS segmentation and MRF for POLSAR images

Speckle effects on classification results can be suppressed to some extent by introducing the contextual information.An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar(POLSAR) images based on the mean shift(MS) segmentation and Markov random field(MRF).First,polarimetric features are exacted by target decomposition for MS segmentation.An initial classification is executed by using the target decomposition and the agglomerative hierarchical clustering algorithm.Thereafter,a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment.Under the MRF framework,the smoothness term is defined according to the distance between neighboring areas.By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory,the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods.

Predictive GIS Model for Potential Mapping of Cu,Pb,Zn Mineralization

The geologic features indicative of Cu, Pb, Zn mineral deposits in a area are fractures (structure), and host rock sediments. Datasets used include Cu, Pb, Zn deposit points record, geological data, remote sensing imagery (Landsat TM5). The mineral potential of the study area is assessed by means of GIS based geodata integration techniques for generating predictive maps. GIS predictive model for Cu, Pb, Zn potential was carried out in this study area (Weixi) using weight of evidence. The weights of evidence modeling techniques is the data driven method in which the spatial associations of the indicative geologic features with the known mineral occurrences in the area are quantified, and weights statistically assigned to the geologic features. The best predictive map generated by this method defines 24% the area having potential for Cu, Pb, Zn mineralization further exploration work.

Photogrammetry Education for Multidisciplinary Geomatics in China

After briefly reviews the history of photogrammetry education in China, the development of undergraduate and graduate program, and the corresponding curricula design are analyzed by use of the data from Wuhan University in which the photogrammetry is awarded as the state-level key discipline. The academic educational program of photogrammetry in universities has trained students to perform tasks in all fields of the photogrammetric profession. In recent years, the nature of photogrammetry is changing and multidisciplinary geomatics are developing very rapidly, the educational program of photogrammetry has also changed in new concepts and structures to adapt very new technologies and the extension of the field. Finally, the prospect of photogrammetry education for the requirements of multidisciplinary geomatics is proposed. The growing interest in fast and accurate 3D spatial data collection (such as city modeling and digital earth) results in the increasing need of photogrammetry as principal tool, photogrammetric courses are therefore requested to be up-to-date and to become one kind of the fundamental professional courses for university geomatics and remote sensing degree programs.

The satellite reversion of dissolved organic carbon (DOC) based on the analysis of the mixing behavior of DOC and colored dissolved organic matter: the East China Sea as an example

The retrieval of dissolved organic carbon (DOC) distribution by remote sensing is mainly based on the empirical relationship of DOC concentration and colored dissolved organic matter (CDOM) concentration in many literatures. To investigate the nature of this relationship, the distributions and mixing behaviors of DOC and CDOM are reviewed in the world's major estuaries and bays. It is found that, generally, the CDOM concentration is well correlated with the salinity in most estuaries, while DOC usually shows a nonconservative behavior which leads to a weak correlation between the DOC concentration and the CDOM concentration. To establish a good satellite reversion of the DOC concentration, the East China Sea(ECS) was taken as an example, and the mixing behavior of DOC and CDOM as well as the influence of biogeochemical processes were analyzed except for the physical mixing process with the data from late autumn (November, 2010) and winter (December, 2009) cruises. In the two ECS cruises, the CDOM concentration was found to be tightly correlated with the salinity, influenced little by the photochemical or biological processes. The data from the winter cruise show that DOC followed a conservative mixing along the salinity gradient, while in the late autumn cruise it was significantly affected by the biological activities, resulting in a poor correlation between the DOC and the CDOM. Accordingly, an improved DOC algorithm (CSDM) was proposed: when the biological influence was significant (Chl a greater than 0.8 μg/dm3 ), DOC was retrieved by the conservative and biological model, and if the conservative mixing was dominant (Chl a less than 0.8 μg/dm3 ), the direct DOC concentration and CDOM concentration relationship was used. Based on the proposed algorithm, a reasonable DOC distribution for the ECS from satellite was obtained in this study, and the proposed method can be applied to the other large river-dominant marginal sea.

Geopotential difference determination using optic-atomic clocks via coaxial cable time transfer technique and a synthetic test

According to the general theory of relativity,two clocks placed at two different positions with different geopotentials run at different rates.Thus one can determine the geopotential difference between these two points by comparing the running rates of the two clocks.Using the most precise optic-atomic clocks whose stability achieves 10^(-18)level and the time transfer technique with comparison accuracy higher than 10ps level by 100 m coaxial cable,the relativistic geodesy method for determining the geopotential may be realizable in the near future.In this paper,we propose,design and describe in detail an approach for determining the geopotential difference between two points based upon a simulation experiment.We select two stations A and B whose ground distance is within100 m,height difference being about 30 m.Each station is equipped with an atomic clock whose instability is 3.2×10^(-16)/τ^(1/2)(whereτis time in second).And the two stations are connected with a coaxial cable for time transfer.Our simulation experiment results show that the accuracy could reach 0.16 m^2/s^2(equivalent to 1.6 cm in height)level after a fourhour period of observation.

Data acquisition,analysis and applications of multi-sensor integration

This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multi-sensor, space-time benchmark for sensor data, and multi-sensor data fusion and mining. Firstly, synchronization control of multi-sensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization sub-controllers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send space-time information to the synchronization sub-controller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, space-time benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. land-borne intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co., Ltd. and has been successfully put into use. The latter is an ongoing research.

Hand Gesture Recognition Using Appearance Features Based on 3D Point Cloud

This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.

Automated Delineation of Smallholder Farm Fields Using Fully Convolutional Networks and Generative Adversarial Networks

Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due to their small size,irregular shape,and the use of mixed-cropping techniques,the farm fields of smallholder can be difficult to delineate automatically.In recent years,numerous studies on field contour extraction using a deep Convolutional Neural Network(CNN)have been proposed.However,there is a relative shortage of labeled data for filed boundaries,thus affecting the training effect of CNN.Traditional methods mostly use image flipping,and random rotation for data augmentation.In this paper,we propose to apply Generative Adversarial Network(GAN)for the data augmentation of farm fields label to increase the diversity of samples.Specifically,we propose an automated method featured by Fully Convolutional Neural networks(FCN)in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution(VHR)images.We first investigate four State-Of-The-Art(SOTA)FCN architectures,i.e.,U-Net,PSPNet,SegNet and OCRNet,to find the optimal architecture in the contour detection task of smallholder farm fields.Second,we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection.We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria.The best combination achieved F1 scores of 0.686 on Test Set 1(TS1),0.684 on Test Set 2(TS2),and 0.691 on Test Set 3(TS3).Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task.The conceptual,theoretical,and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.

FOREWORD FROM THE CHIEF EDITOR The Geo-spatial Information Science mission

Modern scientific research mainly focuses on three themes:materials,energy,and information.The concept of information was coined in the 1920s.Breakthroughs in information technology and the creation of information theory in the 1940s presaged an information age,when research and applications of information theory and technology began in full swing.Since the 1950s,the information revolution wave has swept across the world.By the early 1990s,with a booming information industry,information concepts and information technology permeated all walks of life.Social information networks based on the information super highway have accelerated the pace of construction and development in many countries;the information revolution has demonstrated bright prospects for human beings.

A lightweight distillation CNN-transformer architecture for remote sensing image super-resolution

Remote sensing images exhibit rich texture features and strong autocorrelation.Although the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local information,the limited perceptual field prevents it from establishing long-distance dependence on global information,leading to the low accuracy of remote sensing image reconstruction.Furthermore,it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational demand.In this study,we propose a lightweight distillation CNN-Transformer SR architecture,named DCTA,for remote sensing SR,addressing the aforementioned issues.Specifically,the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and Transformer.In addition,we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation,aiming to select the informative components for better feature fusion.The extracted low-resolution(LR)feature maps are reorganized through the up-sampling module to obtain high-resolution(HR)feature maps with high accuracy to generate highquality HR remote sensing images.The experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets,including NWPU-RESISC45,Draper,and UC Merced.This is achieved by balancing reconstruction performance and network complexity,resulting in both competitive subjective and objective results.

Review of data storage and management technologies for massive remote sensing data

Aiming at the storage and management problems of massive remote sensing data,this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abroad. They mainly include the NASA EOS,World Wind,Google Earth,Google Maps,Bing Maps,Microsoft TerraServer,ESA,Earth Simulator,GeoEye,Map World,China Centre for Resources Satellite Data and Application,National Satellite Meteorological Centre,and National Satellite Ocean Application Service. By summing up the practical data storage and management technologies in terms of remote sensing data storage organization and storage architecture,it will be helpful to seek more suitable techniques and methods for massive remote sensing data storage and management.

Measurements for profiles of aerosol extinction coefficient,backscatter coefficient,and lidar ratio over Wuhan in China with Raman/Mie lidar

The profiles of aerosol extinction,backscatter coefficient,and lidar ratio in the lower troposphere over Wuhan are measured by a multi-channel Raman/Mie lidar.Using the lidar ratio retrieved by Raman scattering principle,the profiles of aerosol extinction and backscatter coefficients are also retrieved by Mie scattering signals,without a prior assumption about their relation in the traditional pure Mie signals data analyses.The observations by both Raman and Mie are in good agreement with each other.The high coherence shows that the system is reliable,and the Mie and Raman channels are in good adjustment and have the same field of view.

基于纠错编码的多变量公钥加密方案(英文)

Advances in quantum computers pose potential threats to the currently used public-key cryptographic algorithms such as RSA and ECC.As a promising candidate against attackers equipped with quantum computational power,Multivariate Public-Key Cryptosystems(MPKCs)has attracted increasing attention in recently years.Unfortunately,the existing MPKCs can only be used as multivariate signature schemes,and the way to construct an efficient MPKC enabling secure encryption remains unknown.By employing the basic MQ-trapdoors,this paper proposes a novel multivariate encryption scheme by combining MPKCs and code-based public-key encryption schemes.Our new construction gives a positive response to the challenges in multivariate public key cryptography.Thorough analysis shows that our scheme is secure and efficient,and its private key size is about 10 times smaller than that of McEliece-type cryptosystems.

Modified aquila optimizer for forecasting oil production

Oil production estimation plays a critical role in economic plans for local governments and organizations.Therefore,many studies applied different Artificial Intelligence(AI)based meth-ods to estimate oil production in different countries.The Adaptive Neuro-Fuzzy Inference System(ANFIS)is a well-known model that has been successfully employed in various applica-tions,including time-series forecasting.However,the ANFIS model faces critical shortcomings in its parameters during the configuration process.From this point,this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer(AO)with the Opposition-Based Learning(OBL)technique.The main idea of the developed model,AOOBL-ANFIS,is to enhance the search process of the AO and use the AOOBL to boost the performance of the ANFIS.The proposed model is evaluated using real-world oil produc-tion datasets collected from different oilfields using several performance metrics,including Root Mean Square Error(RMSE),Mean Absolute Error(MAE),coefficient of determination(R2),Standard Deviation(Std),and computational time.Moreover,the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization(PSO)-ANFIS,Grey Wolf Optimizer(GWO)-ANFIS,Sine Cosine Algorithm(SCA)-ANFIS,Slime Mold Algorithm(SMA)-ANFIS,and Genetic Algorithm(GA)-ANFIS,respectively.Additionally,it is compared to well-known time series forecasting methods,namely,Autoregressive Integrated Moving Average(ARIMA),Long Short-Term Memory(LSTM),Seasonal Autoregressive Integrated Moving Average(SARIMA),and Neural Network(NN).The outcomes verified the high performance of the AOOBL-ANFIS,which outperformed the classic ANFIS model and the compared models.

Progress and challenges in the architecture and service pattern of Earth Observation Sensor Web for Digital Earth

The Earth Observation(EO)Web is the data acquisition and processing network for digital Earth.The EO Web including Data Web and Sensor Web has become one of the most important aspects of the Digital Earth 2020.This paper summarised the history of the development and status quo of the major types of EO data web service systems,including architecture,service pattern and standards.The concepts,development and implementation of the EO Sensor Web were reviewed.Furthermore,we analysed the requirements on the architecture of the next-generation EO Sensor Web system,namely Space-borne-Airborne-Ground integrated Intelligent EO Sensor Web system,and highlighted the virtualization,intelligent,pervasive and active development tendency of such system.

On RPC Model of Satellite Imagery

The RPC model has recently raised considerable interest in the photogrammetry and remote sensing community. The RPC is a generalized sensor model that is capable of achieving high approximation accuracy. Unfortunately, the computation of the parameters of RPC model is subject to the initial of the parameter in all available literature. An algorithm for computation of parameters of RPC model without initial value is presented and tested on SPOT-5, CBERS-2, ERS-1 imageries. RPC model is suitable for both push-broom and SAR imagery.

Ionic composition of submicron particles(PM_(1.0)) during the long-lasting haze period in January 2013 in Wuhan, central China

In January 2013, a long-lasting severe haze episode occurred in Northern and Central China; at its maximum, it covered a land area of approximately 1.4 million km2. In Wuhan, the largest city in Central China, this event was the most severe haze episode in the 21st century. Aerosol samples of submicron particles(PM1.0) were collected during the long-lasting haze episode at an urban site and a suburban site in Wuhan to investigate the ion characteristics of PM1.0 in this area. The mass concentrations of PM1.0 and its water-soluble inorganic ions(WSIIs) were almost at the same levels at two sites, which indicates that PM1.0 pollution occurs on a regional scale in Wuhan. WSIIs(Na+, NH+ 4, K+, Mg2+, Ca2+, Cl-, NO-3and SO2-4) were the dominant chemical species and constituted up to 48.4% and 47.4% of PM1.0 at WD and TH, respectively. The concentrations of PM1.0 and WSIIs on haze days were approximately two times higher than on normal days. The ion balance calculations indicate that the particles were more acidic on haze days than on normal days. The results of the back trajectory analysis imply that the high concentrations of PM1.0 and its water-soluble inorganic ions may be caused by stagnant weather conditions in Wuhan.

Changes in global potential vegetation distributions from 1911 to 2000 as simulated by the Comprehensive Sequential Classification System approach

Vegetation classification models play an important role in studying the response of the terrestrial ecosystem to global climate change. In this paper, we study changes in global Potential Natural Vegetation (PNV) distributions using the Comprehensive Sequential Classification System (CSCS) approach, a technique that combines geographic information systems. Results indicate that on a global scale there are good agreements among maps produced by the CSCS method and the globally well-accepted Holdridge Life Zone (HLZ) and BIOME4 PNV models. The potential vegetation simulated by the CSCS approach has 6 major latitudinal zones in the northern hemisphere and 2 in the southern hemisphere. In mountainous areas it has obvious altitudinal distribution characteristics due to topographic effects. The distribution extent for different PNV classes at various periods has different characteristics. It had a decreasing trend for the tundra and alpine steppe, desert, sub-tropical forest and tropical forest categories, and an increasing trend for the temperate forest and grassland vegetation categories. The simulation of global CSCS-based PNV classes helps to understand climate-vegetation relationships and reveals the dynamics of potential vegetation distributions induced by global changes. Compared with existing statistical and equilibrium models, the CSCS approach provides similar mapping results for global PNV and has the advantage of improved simulation of grassland classes.

Assessment of Soil Erosion and Sediment Yield in Liao Watershed, Jiangxi Province, China, Using USLE, GIS, and RS

Soil erosion by water is a serious problem all over the world. In China, about 1790000 km2 of land suffers from water erosion, which accounts for 18.3% of China’s total area. This study was conducted in the Liao (潦) watershed in Jiangxi (江西) Province to assess annual soil erosion and sediment yield using the Universal Soil Loss Equation (USLE). A geographic information system (GIS) was used to generate maps of the USLE factors, which include rainfall erosivity (R), soil erodibility (K),slope length and steepness (LS), cover (C), and conservation practice (P) factors. By integrating these factors in a GIS, a spatial distribution of soil erosion over the Liao watershed was obtained. The soil erosion was found to vary from nil for flat and well-covered areas to more than 500 t/ha/a in mountainous places with sparse vegetation. The average soil erosion is 18.2 t/ha/a with a standard deviation of 109.3 t/ha/a. The spatial distribution of erosion classes was estimated. About 39.5% of the watershed is under the tolerant erosion rate, and 60.5% of the study area experienced erosion to different extents. A spatially distributed sediment delivery ratio (SDR) module was developed to account for soil erosion and deposition. It was found that the SDR value at the outlet of the Liao watershed was 0.206, and the sediment yield was 1.32 million t/a, which was 20% higher than the measured sediment. The results can be used to identify the soil erosion hot spots and develop the best soil erosion management practices and help estimate the quantity of soil that was transported into the downstream Poyang (鄱阳) Lake.

Efficient Compression of Vector Data Map Based on a Clustering Model

这篇论文为向量数据地图的压缩建议一个新方法。也就是，三关键步在建议方法被包含向量数据的简化经由顶点的消除印射，移开的顶点的压缩基于一个聚类的模型，并且译码压缩向量数据印射。建议压缩方法被实现并且适用压缩向量数据地图以压缩比率和几何形状的失真调查它的性能。结果证明建议方法为向量数据地图的压缩提供一个可行、有效的解决方案并且能完成压缩的有希望的比率并且在压缩向量数据地图以内维持空间对象的主要形状特征。