Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method

The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.

An Integrated Framework for Road Detection in Dense Urban Area from High-Resolution Satellite Imagery and Lidar Data

Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.

Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

Level study on fractal characteristics of tidal creeks and information of seashell habitats in the Gaizhou Beach based on high-resolution satellite images

The fractal characteristics of tidal creeks in the Gaizhou Beach are analyzed based on high-resolution images fusionof Landsat TM and ERS2, and then the graphic models and characteristics of converse information tree of tidalcreeks in the Gaizhou Beach are established. A calculation model is established based on the above results, and at thesame time, quantitative calculation of the evolution characteristics and the diversity between the northern and thesouthern parts of the Gaizhou Beach is carried out. By the supervised classification of these images, distribution andareas of high tidal flats, middle tidal flats and low tidal flats in the Gaizhou Beach are studied quantitatively, and imagecharactistics of seashell habitats in the Gaizhou Beach and the correlation between mudflat distribution and seashellhabitats are studied. At last, the engineering problems in the Gaizhou Beach are discussed.

Arbitrary Scale Super Resolution Network for Satellite Imagery

Recently,satellite imagery has been widely applied in many areas.However,due to the limitations of hardware equipment and transmission bandwidth,the images received on the ground have low resolution and weak texture.In addition,since ground terminals have various resolutions and real-time playing requirements,it is essential to achieve arbitrary scale super-resolution(SR)of satellite images.In this paper,we propose an arbitrary scale SR network for satellite image reconstruction.First,we propose an arbitrary upscale module for satellite imagery that can map low-resolution satellite image features to arbitrary scale enlarged SR outputs.Second,we design an edge reinforcement module to enhance the highfrequency details in satellite images through a twobranch network.Finally,extensive upsample experiments on WHU-RS19 and NWPU-RESISC45 datasets and subsequent image segmentation experiments both show the superiority of our method over the counterparts.

Pinpointing Source of Mekong and Measuring Its Length Through Analysis of Satellite Imagery and Field Investigations

For centuries, explorers and scientists from different countries had made their own conclusions on the source of the Mekong. However, the geographic source of the Mekong is still arguable because of the complexity of the Mekong source water system, inaccessible environment and the varied technologies used by those explorers and scientists. The satellite remote sensing technology has been used to pinpoint the source of the Mekong, associated with the on-the-spot investigations made by the authors in June 1999 and September 2002. The actual length of the Mekong has also been calculated.

Computer Identification of Multispectral Satellite Cloud Imagery

A dynamic clustering method based on multispectral satellite imagery to identify the different features is described. The channel combinations selected are for the different purposes in classification. Several cases are presented using the polar-orbiting satellite imageries.

Change Detection of Lake Chad Water Surface Area Using Remote Sensing and Satellite Imagery

The Lake Chad located in the west-central Africa in the Sahel region at the edge of the Sahara experienced severe drought during 1970s and 1980s and overexploitation (unintegrated and unsustainable use), which is a result of variant land uses and water management practices during the last 50 years. This resulted in a decline of the water level in the Lake and surrounding rivers. The present study analyzed satellite images of Lake Chad from Landsat-MSS, Landsat-OLI to investigate the change of the open water surface area during the years of 1973, 1987, 2001, 2013, and 2017. Supervised classifications were performed for the land cover analysis. The open water area in 1973 was covering 16,157.34 km2 approximately, and that was 64.6% of the total lake area in the 1960s. As an ultimate result of the extreme drought that the study area witnessed through 1970s-1980s, the open water area has decreased to 1831.44 km2, i.e. around 11.33%, compared to that in 1973. The dilemma that the study area is suffering from is believed to be a catastrophic complication of the aforementioned drought crisis, which arose as an ultimate result the climate change, global warming, and the unintegrated and unsustainable use of water challenges the study area is still encountering.

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.

ANALYSIS OF AMBIENT FIELDS AND SATELLITE IMAGERY CHARACTERISTICS OF EFFECT OF BAY OF BENGAL STORMS ON LOW- LATITUDE PLATEAU

Based on the composite analysis method, 12 rainstorms triggered by Bay of Bengal storms (shortened as B-storms hereafter) across the whole province of Yunnan were studied, and some interesting results of rain and circulation characteristics influenced by the storms were obtained for low-latitude plateau. Usually, when a rainstorm weather occurs in low-latitude plateau, the B-storm center locates in the central, east or north parts of the Bay of Bengal. At the same time, the subtropical high ridge moves to 15°N– 20°N and the west ridge point moves to the Indo-china Peninsula from the South China Sea and the low-latitude plateau is controlled by southwest air streams coming from the front of the trough and the periphery of the subtropical high. The southwest low-level jet stream from the east side of the bay storm has great effect on heavy rains. On the one hand, the southwest low-level jet stream is playing the role of transporting water vapor and energy. On the other hand, the southwest low-level jet stream is helpful to keep essential dynamical condition. From the analysis of the satellite cloud imagery, it is found that mesoscale convection cloud clusters will keep growing and moving into the low-latitude plateau to cause heavy rains when a storm forms in the Bay of Bengal.

New Progress in the Application of Satellite Thermal Infrared Imagery to Earthquake Prediction

The concept of stochastic resonance (SR) has been introduced into the analysis of satellite thermal infrared images. Six kinds of anomalous phenomena related to crustal movement were recognized in satellite thermal infrared images. Six diagnostic indicators for the prediction of global earthquakes with magnitude ≥6.0 and their quantitative evaluation standards have been established. The microscopic behavior of global crustal movement is successfully controlled by using satellite thermal infrared imagery, and the occurrence time and magnitude of over 80% of global strong earthquakes occurred since the foundation of the observation station have been successfully predicted. It is believed that the combination of satellite thermal infrared information with macroscopic anomalous phenomena will play an important role in earthquake hazard reduction.

Identification of climatic and environmental factors associated with incidence of cutaneous leishmaniasis in Central Iran using satellite imagery

Objective:To determine the effect of climatic and environmental factors on the incidence of cutaneous leishmaniasis in Qom province in 2018.Methods:In this cross-sectional study,the data on cutaneous leishmaniasis incidence were collected from the Disease Control and Prevention Center in Qom province.Climatic and environmental data including Normalized Difference Vegetation Index(NDVI),Land Surface Temperature(LST),and soil moisture were extracted using satellite images.Data of altitude and sunny hours were provided based on shuttle radar topography mission digital elevation model and hemispherical viewshed algorithm,respectively.The associations of climatic and environmental variables with the incidence of the disease were analyzed by Pearson correlation method.The Arc GIS 10.3 software was used to determine the geographical distribution of these factors.Results:There were positive correlations between cutaneous leishmaniasis incidence and the two climatic factors:LST and sunny hours per day(P=0.041,P=0.016),and it had weak negative correlations with the digital elevation model(P=0.27),soil moisture(P=0.54),and NDVI(P=0.62).The time delay analysis showed that in one-,two-,and three month periods,the correlations increased with a 95%confidence interval.Accordingly,the correlation with the three-month time delay was positive and relatively strong between the cutaneous leishmaniasis incidence and LST and sunny hours(P=0.027,P=0.02);nevertheless,there were negative correlations between the cutaneous leishmaniasis incidence and the soil moisture(P=0.27)and NDVI(P=0.62).Conclusions:As Qom is located in one of the semi-arid climate zones,topography and solar energy are important factors affecting the incidence of cutaneous leishmaniasis in autumn.Therefore,appropriate disease control programs are recommended.

Classifications of Satellite Imagery for Identifying Urban Area Structures

This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.

Intelligent High Resolution Satellite/Aerial Imagery

High resolution satellite images are rich source of geospatial information. Nowadays, these images contain finest spectral and spatial information of ground realities in different electromagnetic spectrum. Many image processing softwares, algorithms and techniques are available to extract such information from these images. Multi spectral as well as panchromatic (PAN) high resolution satellite images are missing, one important information, regarding ground features and realities that information is attribute information which is not directly available in high resolution satellite images. From very first day, this information used to be collected through indirect ways using GPS, digitizing, geo-coding, geo tagging, field survey and many other techniques. Our real world has vertical labels for ground observer to identify and use this information. These vertical labels are present in form of names, logos, icons, symbols and numbers. These vertical labels ease us to work in real world. Satellites are unable to read these labels due to their vertical orientation. Making satellite/aerial imagery rich of attribute information, we have the possibility to design our world accordingly. Just like vertical labels we can also place real physical horizontal label for space sensors, to make this information directly available in high resolution satellite/aerial imagery. This work is about possibilities of such techniques and methods.

Man-Computer Interactive Method on Cloud Classification Based on Bispectral Satellite Imagery

Ｍａｎ－ＣｏｍｐｕｔｅｒＩｎｔｅｒａｃｔｉｖｅＭｅｔｈｏｄｏｎＣｌｏｕｄＣｌａｓｉｆｉｃａｔｉｏｎＢａｓｅｄｏｎＢｉｓｐｅｃｔｒａｌＳａｔｅｌｉｔｅＩｍａｇｅｒｙＹｕＦａｎ（郁凡），ＬｉｕＣｈａｎｇｓｈｅｎｇ（刘长盛）ＤｅｐａｒｔｍｅｎｔｏｆＡｔｍｏ...

Characteristics of an Explosive Cyclone over Northeast China Revealed by Satellite Water Vapor Imagery

In this paper,an explosive cyclone(EC)that occurred over Northeast China in the spring of 2016 is studied by using 6.7μm FY satellite water vapor(WV)imagery and NCEP(1°×1°)reanalysis data.Moreover,the evolutions of the upper-level jet stream(ULJ),the vertical motions,and the potential vorticity(PV)are analyzed in detail.Results show that different shapes of the WV image dark zones could reflect different stages of the EC.At the pre-explosion stage,a small dark zone and an S-shaped baroclinic leaf cloud can be found on the WV imagery.Then the dark zone expands and the leaf cloud grows into a comma-shaped cloud at the explosively developing stage.At the post-explosion stage,the dark zone brightens,and the spiral cloud forms.The whole process can be well described by the WV imagery.The dynamic dry band associated with the sinking motion and the ULJ can develop into the dry intrusion later,which is an important signal in forecasting the EC and should be paid attention to when analyzing the WV imagery.Furthermore,the mechanism is also analyzed in detail in this article.EC usually occurs in the left-exit region of the 200-h Pa jet and the region ahead of the 500-h Pa trough where there is significant positive vorticity advection(PVA).When the EC moves onto the sea surface,the decreased friction would favour the development of the EC.The upper-level PVA,the strong convergence at low level,and the divergence at high levels can maintain the strong updraft.Meanwhile,the high PV zone from the upper levels extends downward,approaching the cyclone.Together,they keep the cyclone deepening continuously.

Spatio-Temporal Analysis of Landuse/Landcover Change of District Pishin Using Satellite Imagery and GIS

Detecting change on the face of the globe using GIS (Geographic Information System) aided by remotely sensed imagery is now becoming an indispensable tool in managing the resources of our planet. The present study with the help of GIS and remote sensing (RS) is also a similar attempt in recording and quantifying change in land use and land cover in district Pishin both in spatial and temporal extents. Satellite imagery was acquired from the USGS official website from three LANDSAT satellites. Theses satellites are LANDSAT 5, LANDSAT7 and LANDSAT 8. The data were acquired for the years 1992, 2003 and 2013. Satellite imagery was processed in ArcMap 10.1 and maximum likelihood supervised image classification was applied in reaching the goal of detecting change. The result of the analysis revealed that built-up area was increased by 5.84%;vegetation was increased by 3.89%;water bodies were increased by 0.05% and bare surfaces were decreased by 9.78%. The decrease in the barren surfaces was attributed to the increase in vegetation and built-up area which replaced the barren land in the study area. This paper also shows the significance and potential of digital change detection methods in managing the resources of our environment and keeping an eye on the land use and land cover of our Earth.

Imagery Data Quality of ZY Satellite Reached International Level

The in-orbit commissioning of ZY-1 02C satellite is proceeding smoothly. According to the relevant experts in this field, the imagery quality of the satellite has reached or nearly reached the level of international satellites of the same kind. ZY-1 02C satellite and ZY-3 satellite were successfully launched on December 22, 2011 and January 9, 2012 respectively. China Centre for Resources Satellite Data andApplication (CRSDA) was responsible for the building of a ground

A new type of Shipboard Meteorological Satellite Receiving-processing System

The Shipboard Meteorological Satellite Receiving-processing System (SMSRPS) is specially developed for the navigation meteorological safeguard of the Antarctic exploration ship over the sea ice area of the forth Ocean. This system can suit to the climate environment of very high temperature high moisture and very low temperature - supersaturation; it has a self-protection ability to against the hurricane - force wind over force 12 and the strong vibration during icebreaking, as well as strong magnetic disturbance. It has two sets of receiving-imagery processing systems for polar orbit low-resolution and quasi-stationary high-solution satellites. The key creation Points of this system are as follows: 1. the active gyro-control stabilization platform and a mixed mounting system of three rotating a - B and x -- y axes are used. It solved the tracing difficulties both in the low elevation angle and very high elevation angle of polar-orbit satellite, even in the status of ship moving with continuously changing its poition, direction and ship roll and pitch. 2. Imagery processing subsystem. The newest BORLAND-- DELPHI language and PASCAL language pro gramming software are used under WINDOWS 95 environment. It has a dynamic positioning nested-grid system and electric mapping grid data system. It can show the latitude-longitude of any point on the map, and marks any object such as ship, station or island, and draws the route. It can monitor cloud and temperature, forest fire, anomalous change of ocean and land. It can output satellite cloud maps of 24 bit with very high clarity. This system is very advanced in technique for the whole structure with the features of small volume, light weight and very low cost. It suits to very bad climate and ocean environment. Its imagery process ing system has complete functions with high resolution and being very easy to operate. It is not only suit to land use, but also and specially to all kinds of ship over the sea. It can be extended to domestic and international use. This system played a very important role in the 14th Chinese Antarctic Exploration Navigation, and was introduced a broad attention paid by Chinese newspapers and TV Stations.

Investigation on the Relationship between Population Density and Satellite Image Features——a Deep Learning Based Approach

Timely and accurate population statistic data plays an important role in many fields.To illustrate the demographic characteristics,population density is a crucial factor in evaluating population data.With a dynamic regional migration in population,it is a challenging job to evaluate population density without a census-based survey.We present the approach to classify satellite images in different magnitudes in population density and execute the comparative experiment to discuss the factors that influence the identification to the images with the deep learning approach.In this paper,we use satellite imagery and community population density data.With convolutional neural networks,we evaluated the performance of CNN on population estimation with satellite images,found the features that are important in population estimation,and then perform the sensitive analysis.