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Design of Controller Board and DC-DC Interface for Remote Sensing Satellites

Design of Controller Board and DC-DC Interface for Remote Sensing Satellites

applied to the chip and generates reset impulses is given to the FPGA circuit. MIL-STD-1553 bus is used for communication between storing elements and buffers. For remote sensing satellites the controller and DC-DC interface is designed. The controller unit is designed for both main and redundant controller. The RAW bus voltage received by the controller is converted into secondary voltages with the help of DC-DC converter. And the over voltage in the circuit is handled by the OVP (Over Voltage Protection). The EL215 relay driver is used to select the secondary voltages using commands given from FPGA. The MIL-STD-1553 bus communication is used as normal bus communication is complex.
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Data Processing System for Remote Sensing Satellites in Private Cloud Environment

Data Processing System for Remote Sensing Satellites in Private Cloud Environment

The Remote Satellite Imagery servers as a primary source used to monitor the environmental conditions or to detect the responses of upcoming disasters and also to plan mostly the terrain infrastructures. The photographs from satellite are taken in digital form and later are processed by the servers to extract the meaningful information. Several analytical and statistical image processing algorithms are applied to the digital images and after processing the various discrete surfaces are identified in terrain and aquatic regions and by analyzing the pixel values. Advanced earth observations technologies are used to cater the needs of remote sensing data. These technologies produce more variety and huge datasets, the processing of these data needs a modular framework focusing the concepts of compute-intensive and data-intensive environment. The hardware and software considerations are always outlined along with the mission related data processing requirements. Therefore, a paradigm that fits into the road to exascale is considered to be Cloud Computing.
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IHS based Pan Sharpening with Geo-Image Processing Alpesh R. Sankaliya

IHS based Pan Sharpening with Geo-Image Processing Alpesh R. Sankaliya

Spatial data are captured by some remote sensing satellites. These satellites actually capture many bands of images. Now, these captured images are put under different types of processing in order to get the useful image for further usage. This phenomenon is called as Geo – Image Processing.

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Railway Transport Infrastructure Monitoring by UAVs and Satellites

Railway Transport Infrastructure Monitoring by UAVs and Satellites

DOI: 10.4236/jtts.2019.93022 348 Journal of Transportation Technologies The same area near the water tower was shot from the board of the UAV equipped with a Canon EOS 5D Mark II camera with EF-S 17-55 f/2.8 IS USM lens. Camera was mounted on a gimbal controlled by an operator, Figure 1. The result of the photographing from flight altitude about 70 m is shown in Figure 4. The difference between these two images consists in the location of the rail wa- gons on the tracks near the water tower that caused by different time of the shooting. The satellite image was recorded in summer, and drone flights were performed in autumn. They also differ in size and direction of the local objects shadow, as the images were taken at different positions of the sun on the celestial sphere. This fact stresses the importance of sun-synchronous orbits, which were mentioned earlier and are commonly used for remote sensing satellites. Another advantage of using sun-synchronous orbits is the ability to determine whether an object is being built or not, and at what speed it is being built, judging by the length of the shadow at the images obtained from different orbit passes of the satellite. Usually this method is used for the interpretation of images recorded by military reconnaissance satellite, but it also can be used for cadastral surveying, when it is necessary to allocate from a large array of images new and illegally erected buildings. In that case, to determine the newly erected buildings and their height change it is enough to subtract the images.
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Remote sensing of zooplankton swarms

Remote sensing of zooplankton swarms

Remote sensing of ocean color has led to major advances in understanding ocean-wide phytoplankton dynamics by providing much greater temporal and spatial coverage than is possible by ship-based sampling. Phytoplankton species rely on pigments for photosynthesis and other photophysiological processes. These different colored pig- ments affect the spectral characteristics of light redirected out of the sea and so becoming accessible to remote sensing detectors 1,2 . Surface-dwelling zooplankton, on the other hand, do not rely on capturing energy through

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Change Detection in Remote Sensing Images Using Elitist Genetic Algorithm

Change Detection in Remote Sensing Images Using Elitist Genetic Algorithm

This paper presents a simple but effective method for change detection. The effectiveness of the proposed method has been proven by the experimental results on images from a standard image database. There are lots of scopes of experimenting with remote sensing images, document images, analysis of medical images and change detection in remote-sensing images, testing the noise immunity of the proposed method etc.

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Vehicle Detection In Remote Sensing Images

Vehicle Detection In Remote Sensing Images

Urban communities everywhere throughout the world manage a large number of vehicles going through their roads every day. The expanding number of vehicles is viewed as the primary reason for traffic jam. This makes it hard likewise to discover a spot to park the car. The greatest problem in these years is the security and the activity control of the roadways, attempting to make individuals life less demanding. Numerous answers for taking care of this issue is proposed. One way is utilizing remote sensing field, particularly Unmanned Aerial Vehicle (UAV) which these days picks up an awesome fame in this field.
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Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD+

Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD+

Mapping forest degradation with remote sensing data is more challenging than mapping deforestation [29] because the degraded forest is a complex mix of differ- ent land cover types (vegetation, dead trees, soil, shade) and the signature of the degradation often changes within 1-2 years [30-32]. So far, to address forest degra- dation, medium spatial resolution sensors, such as Land- sat, ASTER and SPOT, have mostly been used for degradation mapping. High and very high resolution satellite imagery, such as Ikonos or Quickbird, and aerial digital imagery acquired with videography have also been used. Methods for mapping forest degradation range from simple image interpretation to highly sophis- ticated automated algorithms [10].
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Remote sensing of aerosols with small satellites in formation flight

Remote sensing of aerosols with small satellites in formation flight

Abstract. Determination of aerosol optical properties with orbital passive remote sensing is a difficult task, as obser- vations often have limited information. Multi-angle instru- ments, such as the Multi-angle Imaging SpectroRadiome- ter (MISR) and the POlarization and Directionality of the Earth’s Reflectances (POLDER), seek to address this by making information-rich multi-angle observations that can be used to better retrieve aerosol optical properties. The paradigm for such instruments is that each angle view is made from one platform, with, for example, a gimballed sen- sor or multiple fixed view angle sensors. This restricts the observing geometry to a plane within the scene bidirectional reflectance distribution function (BRDF) observed at the top of the atmosphere (TOA). New technological developments, however, support sensors on small satellites flying in forma- tion, which could be a beneficial alternative. Such sensors may have only one viewing direction each, but the agility of small satellites allows one to control this direction and change it over time. When such agile satellites are flown in formation and their sensors pointed to the same location at approximately the same time, they could sample a distributed set of geometries within the scene BRDF. In other words, ob- servations from multiple satellites can take a variety of view zenith and azimuth angles and are not restricted to one az- imuth plane as is the case with a single multi-angle instru- ment. It is not known, however, whether this is as potentially capable as a multi-angle platform for the purposes of aerosol remote sensing. Using a systems engineering tool coupled with an information content analysis technique, we investi- gate the feasibility of such an approach for the remote sens- ing of aerosols. These tools test the mean results of all ge- ometries encountered in an orbit. We find that small satellites in formation are equally capable as multi-angle platforms for
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A Review of Geological Applications of High-spatial- resolution Remote Sensing Data

A Review of Geological Applications of High-spatial- resolution Remote Sensing Data

Abstract: Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. By analyzing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images using literature from the Science Citation Index reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, geological remote sensing research needs to be truly multidisciplinary or interdisciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved automatic processing and data analysis capabilities.
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Online Full Text

Online Full Text

Abstract—This paper discusses the robust classification for large data, in case classification of vegetation area at Jakarta with remote sensing. Remote Sensing is the process involving an interaction between incident radiation and the targets of interest. The classification process is guided in two steps; training and classification steps. The training step is done to know the reference spectral of vegetation area, and the classification step is carried out to clasify the Jakarta area into the vegetation and the non vegetation area. The hole process of classification is not simple. The main problem is noise. The claud covering area is considered as noise. The classification of large data with noise needs the efficient and effective approach. The aim of the paper is to propose a new robust approach , the Modified MVV, to classify the vegetation area of Jakarta. The Modified MVV is the modified data subset having minimum of a square of length of a parallelotope diagonal . The good properties of Modified MVV are the consistent estimator and the more efficient computational time than is of MVV.
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Role of Remote Sensing and Geographic Information System (GIS) in Disaster Management in Arunachal Pradesh: a Case Study of Landslides in Itanagar Capital Complex

Role of Remote Sensing and Geographic Information System (GIS) in Disaster Management in Arunachal Pradesh: a Case Study of Landslides in Itanagar Capital Complex

ABSTRACT: Remote sensing and Geographical Information System (GIS), has provided enormous scope for geohazard studies and disaster management. In Arunachal Pradesh landslides, flash floods, landslide dam outburst floods (LDOF) and earthquakes are the major geohazards. Itanagar, the capital city witnessed insidious rainfall- triggered landslide incidences in the past decade. Anthropogenic-forcing due to significant rise in population and haphazard urbanization has resulted in accumulation of landslide risk in the capital city. The Landslide Hazard Zonation (LHZ) maps on 1:50K scale are generated under the DMIS project. These maps form very important macro- scale information for all the developmental progammes in the State. Depending on the severity of the landslide hazard zones various actions were suggested like forest conservation, channelization of water flows in the areas affected by existing slides, etc. High resolution topography points to the changes in geomorphic and anthropogenic features that have affected the landscape of the city area therefore Disaster Management and mitigation in Itanagar capital complex demands a holistic way, linking society with the protection of natural ecosystems and appropriate land and water uses. A prospective and compensatory Disaster Management (DM) programme is needed for attaining Disaster resiliency in Itanagar capital complex.
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An Efficient Active and Passive Security Protection Method for Images

An Efficient Active and Passive Security Protection Method for Images

The primary goal of the present work was to develop a method for the secure transmission of images. There are di erent techniques to give comprehensive protection for image separately. It is proposed that integrating di erent technologies gives better result. Preprocessed (with DCT) images were successfully decomposed orthogonally the with the help of orthogonal transforms. The two technologies of encryption and watermarking were integrated independently into the same image and any type of encryption and watermarking technology can be applied. The experimental data supported our hypothesis- on analysis it was found that proposed method maintained the quality of retrieved image. Experiments on collected data set show that quality of the image did not degrade by integrating encryption and watermarking. Images of general interest, remote sensing data etc are to be protected during image transfer. On comparison with other methods, it was observed that better performance in terms of image quality was achieved by this new approach. Performance of the proposed method highly depends on the transform that is used for orthonal decomposition and preprocessing. Apropriate transforms which gives better result has to be chosen by the observing changes in the result. In the case of remote sensing data quality improvement of images are essential, so better transforms have to be used to improve the performance
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Spatial and Temporal Variability of Moisture Condition in Soil-Plant Environmet using Spectral Data and GIS Tools

Spatial and Temporal Variability of Moisture Condition in Soil-Plant Environmet using Spectral Data and GIS Tools

The use of remote sensing techniques in the analyses of droughts is based mainly on employ- ing spectral indicators for determining the condi- tion of the vegetation (vegetation health) [Nico- lai-Shaw et al. 2017, Dąbrowska-Zielińska et al. 2011]. Connecting the reflection of light with the biophysical characteristics of plants enabled to use the spectral indicators for assessing biomass production, forecasting of yields, and susceptibil- ity of plant organisms to the influence of stressors such as water retention in the soil. It is possible to use plants as indicators in the study of soil-plant environment moisture [Dąbrowska-Zielińska et al. 2011], and thus in the assessment or even pre- diction of drought. Normalized Difference Vege- tation Index (NDVI) [Pettorelli et al. 2005, Huete et al. 2002] is one of the most recognized and de- scribed spectral indices. This index is calculated based on the difference between reflectance of sunlight in the near–infrared and red bands nor- malized by the sum of both of them [Wójtowicz et al. 2005]. Its basic function is determining the amount of chlorophyll in the plants cells. Accord- ing to this parameter, it is possible to determine the condition of the plant and its susceptibility to external stressors [Carlson and Ripley 1997]. NDVI can be used as a substitute indicator for as- sessing the water content in the environment be-
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Technical Note: Use of remote sensing for landslide studies in Europe

Technical Note: Use of remote sensing for landslide studies in Europe

The answers to the questionnaire show that remote sens- ing techniques are affected by some limitations and critical points, which have to be addressed by the researchers and technicians to increase the effectiveness of the landslide de- tection, mapping and monitoring. The main challenge in this regard is the improvement of the spatial resolution and the shortening of the revisiting time. The Cosmo–SkyMED (in X band) constellation, which was launched in 2007, has a short revisiting time (12 h in the better case), such as ALOS mission (in L band) launched in 2006. Nevertheless, time is needed before these missions will provide achievements that could be effectively employed in multi-temporal pro- cessing. Improvements are also expected in optical imagery from the development of the Pleiades program by the CNES: it is tailored on civil security applications and it consists of two small satellites (the first of which is currently operat- ing and has been since the end of 2011, while the second one is scheduled for the end of 2012) with a spatial resolu- tion at nadir of 0.7 m with a daily access all over the world and a stereoscopic acquisition capacity. The full operational use of all the satellite missions and the new ones already on board is outstanding to retrieve data and information tailored to the user’s requirements in terms of short revisiting time, high spatial resolution and cost/benefit ratio. Lastly, enhance- ments and optimizations of new software and algorithms are expected in order to reduce computational time and to in- crease automation and robustness to avoid as much subjec- tive judgment as possible.
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Studying snow cover in European Russia with the use of remote sensing methods

Studying snow cover in European Russia with the use of remote sensing methods

with the ground-based SWE data from meteorological stations for long period, the impact of variations of basic meteorological elements on the data accuracy was estimated; also the major factors influencing the uncertainty were revealed and estimated. The errors of the SWE estimate (evaluated as relative root-mean-square deviations) are maximal for the Northern Dvina watershed, the average error being 53%; for the Oka and Don river basins, the errors are 35 and 33%, respectively. The main problems of remote sensing for northern river basins are due to the thick canopy and high snowpack, whose height exceeds the penetration depth. For the southern regions and midland river basins, a priority problem is the presence of liquid water in the snowpack during thaws and inverse relation between the estimated SWE and the respective air temperature. Furthermore, calculations have shown that the uncertainty also depends on the number of ground-based snow courses. Those factors are taken into account in the initial algorithm of SWE retrieval, but, because of some special features of the studied territory, their re-estimation is required.
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Enhancement of the Satellite Images using ACO and DNN Approach

Enhancement of the Satellite Images using ACO and DNN Approach

Satellite Image classification is a procedure of collecting pixels into meaningful categories. It is a multi-phase work flow. It can also refine as extracting information from satellite images. It is not difficult, but the study has to take several decisions and selections in satellite image classification procedure. It involves in interpretation of remote-sensing images, data mining and analyzing several vegetation kinds such as foresters and agriculture images etc.[9,10]

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Volume 9 Issue 1 January 2020 | International Journal of Science, Engineering and Technology Research

Volume 9 Issue 1 January 2020 | International Journal of Science, Engineering and Technology Research

In order to study a landslide, we need to be able to view the size and contrast of the landslide features and the morphological expression of the topography within and around the landslide. Parameters we might be interested in determining are the type of movement that has occurred, the degree of present activity of the landslide, and the depth to which movement has occurred. The most common remote sensing tools used for the detection and classification of landslides are aerial photographs. Aerial photography has become a standard tool aiding in the study of landslides.

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Remote Sensing Walker

Remote Sensing Walker

Traditionally, a handicapped walker is picked up and placed a short distance ahead of the user. The user then walks to it and repeats the process. If the elders or handicapped person have to move using manual walker, they must raised the walker step by step where it is very difficult and requires more energy to the user. Using the remote sensing walker, the users only need to push the switch at RF remote control and on the walker frame to move in various directions that are left, right, forward, and reverse. It also provides two photo-electric sensors and ability to detect the obstacle in front. This walker is easily park or return to the user where it only need the depressing of the switch at RF remote control and it also facilitate the user to go to anywhere without inconveniencing other people.
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RESEARCH ARTICLE Remote sensing is a viable tool for mapping soil salinity in agricultural lands

RESEARCH ARTICLE Remote sensing is a viable tool for mapping soil salinity in agricultural lands

In the past decade, efforts to map soil salinity at re- gional scales and characterize its spatial variability have focused on the use of predictor covariates that can be observed remotely with continuous spatial coverage across a region (e.g., Lobell et al. 2007). Remote sensing is ideal for identifying within-field variability, which is known to exist in the farmland of the WSJV (e.g., Lesch et al. 1992). This remote-sensing approach is in contrast to traditional methods of assessing soil salinity by soil sampling, which are typically carried out at coarse resolution (e.g., soil samples every ~ 1,000 to 1,500 yards). In their recent soil survey reports (e.g., Arroues 2006), the Natural Resources Conservation Service (NRCS) provided salinity estimations only for nonir- rigated soils because the influence of irrigation on soil salinity cannot be accounted for at the regional scale using traditional soil survey protocols. Remote sens- ing, however, is able to capture abrupt changes between neighboring fields that have the same soil type but are managed differently (fallow vs. irrigated, drip vs. flood irrigation).
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