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A Secure and Efficient Method for Remote Sensing Imagery Distribution in Cloud Computing

A Secure and Efficient Method for Remote Sensing Imagery Distribution in Cloud Computing

However, although re-encryption can prevent information leakage in the cloud computing, the con-tent owner does not prevent an authorized user’s illegal replication or delivery of the content after the encrypted RSD is legally decrypted [4-5]. Such illegal dealings can seriously harm a content owner’s copy-right. Therefore, copyright protection in cloud computing is also a critical issue. Traditional marking techniques can protect ownership rights of the DOs [6-8], but they would cause severe effects on complete remote sensing images (RSIs), such as edges of a RSI [8]. These effects hinder further explorations and RSI applications. Reversible Marking (RM) technique provides a solution to solve the above problem. RM technique is able to erase the distortion introduced by embedding step after cover resto-ration [9]. Marking information embedded into RSI can be used to protect owner’s copyright. The marked RSI can also satisfy some common applications. Furthermore, to fully satisfy the further exploration of the RSI, the marking information can be fully extracted and the RSI can be recovered.
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Cloud Detection via Convolutional Neural Network in Visible Light Remote Sensing Images

Cloud Detection via Convolutional Neural Network in Visible Light Remote Sensing Images

Abstract. Cloud detection in visible light remote sensing images is a challenge due to only few RGB spectral channels and multiple types of clouds and land covers. In this paper, we propose a new method based on convolutional neural network for cloud detection. Our method automatically learns the intrinsic features of the clouds and the other land covers, and can produce high accuracy for cloud detection. The learned features in our method are then visualized and explained in detail, and compared with other features in a baseline method. Finally, experiments are conducted to demonstrate the effectiveness of our method.
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A cloud-based remote sensing data production system

A cloud-based remote sensing data production system

the scheduling strategy of each production task mainly depends on the Ganglia monitoring results, thus the computing resources can be allocated or expanded adaptively. Finally, we evaluated the proposed programme using test exper- iments performed at global, regional and local areas, and the results showed that our proposed cloud-based remote sensing production system could deal with massive remote sensing data and different products generating, as well as on-demand remote sensing computing and information service.

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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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Ground-based remote sensing scheme for monitoring aerosol–cloud interactions

Ground-based remote sensing scheme for monitoring aerosol–cloud interactions

An ample number of studies have been made to quantify the impact of aerosol concentration on cloud microphysical properties. Studies focusing on low-level liquid water clouds are often based on different methods and instruments. Be- cause of this, the temporal and spatial resolution vary signif- icantly. Observational studies of the aerosol effect on clouds use surface remote sensing instruments at specific locations (e.g. Feingold et al., 2003; Schmidt et al., 2014) or rely on a combination of both surface remote sensing and aircraft in situ observations (e.g. Garrett et al., 2004; Kim et al., 2008; McComiskey et al., 2009). To characterise the aerosol effect on a global scale, many research studies focus on the satellite remote sensing observations (e.g. Kaufman et al., 2005). Mc- Comiskey and Feingold (2012) summarised the broad scope of different methods and scales used. They concluded that a single measure of aerosol–cloud interactions (ACIs) used in climate model estimates of the radiative forcing yields widely fluctuating results. ACI is a single measure derived from observational data from varying scales and different as- semblies of instruments. Further, they concluded that ACI r
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Spectral and Spatial Cloud Detection Onboard for Hyperspectral Remote Sensing Image

Spectral and Spatial Cloud Detection Onboard for Hyperspectral Remote Sensing Image

The following three different accuracies measures, which were the overall accuracy, precision and recall, were used to assess the accuracy of the algorithm results. Define the True Positive (TP) as the pixel-number of clouds correctly labeled as belonging to clouds in the algorithm, False Negatives (FN) as the pixel-number of clouds incorrectly labeled as belonging to non-cloud, and True Negatives (TN) as pixel-number of non-cloud which also labeled as belonging to non-cloud. The accuracy, precision and recall are then defined as

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Cloud tolerance of remote sensing technologies to measure land surface
temperature

Cloud tolerance of remote sensing technologies to measure land surface temperature

olution, the overall unbiased RMSE between TIR-LST and ground stations during clear-sky days is 2.1 K for the four lo- cations in the MSG domain, and 2.8 K for the nine locations in the GOES domain. For the same locations we find that the MW-LST is only slightly higher ( + 0.2 K for both domains). With increasing cloudiness the RMSE increases signifi- cantly for TIR-LST, caused by a matching negative trend in bias that is seen at all seven locations. This demonstrated the known effect that clouds have on TIR estimates of LST. The fact that these trends are so apparent highlights the lim- itations of current cloud screening techniques as employed in TIR-LST products that are in general use. In clear con- trast to this we find a much more limited response in both RMSE and bias for MW-LST. Because of this we conclude that there is no significant direct impact of clouds on the ac- curacy of the MW-LST product. However, at three stations the size and sign of the response is such that further research is needed to identify the exact causes introducing error in MW-LST. By taking into account the atmospheric humid- ity and temperature profile, further analysis may investigate the extent to which this mixed response can be explained by atmospheric conditions associated with cloudiness. Alterna- tively, if a greater database were available of locations with flux tower sites in contrasting land covers, this could be used to isolate the role of scale mismatch between station and the satellite product.
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Remote sensing of multiple cloud layer heights using multi-angular measurements

Remote sensing of multiple cloud layer heights using multi-angular measurements

Here we investigate how the method performs for varying COTs. Passive sensors are typically less sensitive to optically thin clouds, so it is important to know the accuracy of the RSP’s ability to retrieve heights of clouds with low optical thicknesses. The CPL is capable of routinely sensing opti- cally thin clouds and is able to accurately sense multilayered cloud scenes up to a total optical thickness of about 3. How- ever, lidars are unable to sense cloud base of optically thick clouds or any clouds underneath. All of the comparisons start by using RSP-derived cloud heights; even as the layer opti- cal thicknesses decrease, comparisons are only done when the RSP senses a layer, and there are likely instances not re- flected in this assessment when CPL senses a thin layer that the RSP does not sense. For this part of the investigation, the baseline filtering described in Sect. 4.1 is used. Figure 9a shows the relation between the CPL optical thickness and the RSP cloud height error for all layers with calculated op- tical thicknesses. All bins are 0.25 wide except the last bin, which represents layers with optical thicknesses greater than 3.0. For the first layer, each of the bands’ errors remain rel- atively constant throughout the range of COTs even for lay- ers with an optical thickness below 0.1. If the RSP detects a layer, even of low optical thickness, it is consistent in its ability to determine the layer’s height. There are many cases where CPL senses two or more layers and the mode separa- tion difference is only 1 km, so it is possible that more than one CPL layer can be contributing to RSP’s retrieval. The errors have a slight, gradual increase with increasing optical thickness for the second and third layer. For clouds with op- tical thickness between 2.75 and 3.0, the difference between CPL and RSP heights is larger than for thinner clouds for
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Control on Remote Sensing Network using Cloud Computing Services

Control on Remote Sensing Network using Cloud Computing Services

 Power and energy Storage Technology: The applications and autonomous things that are running on the IoT that perform the monitoring or sensing activities require the continuous supply of power and energy from the power to perform desired job and the power can be obtained by different means like solar, vibration, heat etc,. And the power collection technology varies according to the environmental conditions and the power generation and storage technologies are the enablers for the deployment of IoT applications. Hence the power generation technologies should provide a high end power generation and harvesting techniques and one among which support today’s IoT is nano electronics.
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Remote sensing

Remote sensing

et al. 2015) of the European Space Agency (ESA) and Japan Aerospace Exploration Agency (JAXA) to be launched in 2018 will eventually combine the capabilities of spaceborne lidar and radar into one satellite. Combining sophisticated methods of ice detection in clouds with lidar and radar will be the starting point of a development that will enable us to follow the life cycle of a heterogeneously formed ice particle from the ice nucleus (Mamouri and Ansmann 2015), to ice nucleation, and toward the generation of rain. This devel- opment has already started, for example, with advanced CALIPSO/CloudSat products like radar–lidar cloud pa- rameter retrieval (DARDAR; Delanoë and Hogan 2010; Battaglia and Delanoë 2013) and the EarthCARE mission. It is an intention of this chapter to show that ground-based, airborne, and satelliteborne remote sensing instruments can deliver critical information about height levels of ice forma- tion and the history of cloud ice and can, hence, be used to challenge the problems mentioned above. The length scales that can be observed by satellites are usually larger than for ground-based instrumentation, limiting the capability to di- rectly observe cloud processes. Most ground-based remote sensing instruments can also be operated with limited effort on a continuous basis. During the recent two decades, com- bined remote sensing studies have been used for decades for long-term monitoring programs. Large efforts have been put into the development of frameworks such as the Atmo- spheric Radiation Measurement (ARM) program (Shupe et al. 2008; Miller et al. 2016) or Cloudnet (Illingworth et al. 2007) that process combined remote sensing data automati- cally and provide quality-assured data to users on an opera- tional basis. For ground-based cloud radar systems, the development of innovative methods to detect ice-formation processes has occurred (Kollias et al. 2014; Myagkov et al. 2015, 2016), including the use of the wavelength dependence of radar attenuation. Such approaches will deliver new in- sights into the microphysical composition of clouds, including detailed information on particle size and shape (Smith et al. 2007; Kneifel et al. 2011). Recently, methods were developed to measure the amount of ice nucleating particles with ground-based Raman–depolarization lidar (Mamouri and
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Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate

Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate

Abstract. Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction o f Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution o f cloud contaminations, are always a critical task in the tropical climate. This paper suggests a fast and alternative approach to remove cloud and shadow contaminations for Landsat Enhanced Thematic Mapper+ (ETM+) multi temporal datasets. Band 3 and Band 4 from all the Landsat ETM+ dataset are two main spectral bands that are very crucial in this study for cloud removal technique. The Normalise difference vegetation index (NDVI) and the normalised difference soil index (NDSI) are two main derivatives derived from the datasets. Change vector analysis is used in this study to seek the vegetation dynamics. The approach developed in this study for cloud optimizing can be broadly applicable for optical remote sensing satellite data, which are seriously obscured with heavy cloud contamination in the tropical climate.
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Remote sensing of zooplankton swarms

Remote sensing of zooplankton swarms

Satellite images. Real-time support from NEODAAS (NERC Earth Observation Data Acquisition and Analysis Service) provided RGB images and NetCDF data files from daily (cloud-free) VIIRS satellite overpasses, which were used to map water masses with distinct spectral signatures and provided a first indication on the location of red, i.e. potentially Calanus-rich, patches. NEODAAS also provided weekly composite images such as that shown in Fig. 1 (RGB) and 5 (chlorophyll a), from 27 April to 3 May 2017. April 27–29 contributed most to the weekly average image, due to relatively high percentage of cloud cover on the later days. To generate the RGB image, wavebands 551 nm, 486 nm and 443 nm were used, and each color was scaled to include 95% of all pixels. A gamma correction of 0.9 was applied to the blue channel to improve contrast. To illustrate the differ- ences in spectral R rs , three areas (three times three pixels from each area) with three different colors were selected
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Design and Development of Framework to Create Cloud Free Reference Repository of IRS Satellite Images for Image Analysis

Design and Development of Framework to Create Cloud Free Reference Repository of IRS Satellite Images for Image Analysis

Technological advancement and increasing availability of high-resolution satellite images, offer the potential for more accurate analyses, by which the detection and quantification of changes could improve greatly. Such remotely-sensed products are expensive and difficult to acquire, which not only prohibits but restricts their use to minimum. This paper aspires to create a reference repository of cloud free satellite images using data received from on board optical sensors of Indian Remote Sensing satellite. By treating the received data with radiometric and geometric correcting processes at various levels, clear images are generated to user environment for early detection of anomalies, mostly during the first level of processing itself. A reference repository will be created using radio-metric cloud free sub- sampled raw images of size 1K x1K, along with terrain classification tags. Despite, the reference repository framed can be used for developing reference based change algorithms and alerting data product generation system for scene specific corrections, radiometric imbalances, product quality degradation etc. The created reference repository in this paper contains abundant database with spatial data types, data structures and features. Also the sample is suitable for queries on geographic applications and may be utilized in clarifying knowledge-based queries, as a part of User Order Processing System (UOPS)..
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Adoption of M- Wallet: A way Ahead

Adoption of M- Wallet: A way Ahead

It was barely a year ago that Uttarakhand, another Himalayan state, was devasted by floods. In Kashmir, the rainfall was unexpected and extremely high, and authorities had ignored warnings. A report prepared by Department of Environment, Ecology and Remote Sensing (DEERS) in collaboration with Hyderabad-based National Remote Sensing Centre, ISRO reveals that the floods in Jammu and Kashmir are a result of High rainfall in the catchments over short period of time, which were not less than cloud bursts and is a combine effect of the extreme event due to climate change and less capacity of our drainage system that failed to hold the quantum of water and it overflowed, which ultimately lead to floods The human tragedy and property loss in both Uttarakhand and Kashmir was enormous. The fact behind the both Himalayan states is that there is evidence proving a change in global weather patterns and its natural variability, i.e., climate change, brought about by man-made carbon emissions is heating up the atmosphere faster than normal.
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Ice crystal number concentration from lidar, cloud radar and radar wind profiler measurements

Ice crystal number concentration from lidar, cloud radar and radar wind profiler measurements

Today, all remote-sensing approaches for retrieving N need a priori information about crystal size before it is possi- ble to be able to derive N. Extensive observational variables like lidar-derived optical particle extinction E or radar re- flectivity factor Z can then be used to retrieve N. Hence, the task of deriving an estimation of particle size is cen- tral to deriving N . Different methods exist for retrieving a proxy for particle size. Mitchell et al. (2018) use a combi- nation of active and passive remote-sensing sensors in or- der to constrain the properties of the observed cloud parti- cles. Cazenave et al. (2019), Delanoë and Hogan (2010), and Sourdeval et al. (2018) employ a forward-iteration method in order to obtain an estimation of N from combined observa- tions of spaceborne lidar and cloud radar. Employing these techniques, an estimation of N based on assumptions (e.g., on particle shape) is well constrained if both lidar and cloud radar (CR; Görsdorf et al., 2015) observations are available. This method exploits the strong wavelength dependence in the efficiency of the backscatter signal between lidar (geo- metrical scattering) and radar (Rayleigh/Mie scattering) for ice particles. In optically thick clouds, where only CR mea- surements are available, the method falls back to parameter- izations of particle size, and the retrieval of N is no longer
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Vol 3, No 7 (2015)

Vol 3, No 7 (2015)

pervasive systems as an opportunity to bring stronger security to pervasive systems. If cyber security researchers are involved in the evolution of this process, they can influence the process and change the current cyber security battlefield to one more amenable to the defenders. Advancements in virtual machine isolation, homomorphic encryption, client authentication, resource management, and secure opportunistic computing will facilitate the adoption of cloud computing while ensuring greater security and privacy for users.We propose cloud-based remote sensing algorithms for enabling distributed estimation of unknown parameters via sensor network virtualization. The algorithm has the following phases: sensor search, domain pruning, benefit matrix construction, virtual-participatory sensor assignment solver, and distributed estimation. Using simulation, we show that the proposed algorithms reduce communication overhead significantly without compromising the estimation error when compared to the traditional ADMM algorithm. We also show that the convergence time of our proposed algorithms maintain linear convergence behavior, as in the case of conventional ADMM.
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Unmanned Aerial Vehicle Remote Sensing Monitoring And Image Acquisition System

Unmanned Aerial Vehicle Remote Sensing Monitoring And Image Acquisition System

The performance of UAV remote sensing system is excellent, can according to the scheduled flight routes, flight shooting, high control precision, stable flight attitude and flight height from 50 meters to 4000 meters, 10 meters high control precision, speed range from 70 km / h to 160 km / hour, can be a smooth flight, in complex weather conditions Sichuan Province, cloudy and foggy etc., can be carried out under the cloud of aerial flight, which can work as far as possible to ensure timely and accurate completion. UAV remote sensing system of flight operation and high automation, simple operation, and automatic fault diagnosis and display function, and easy to master training, once the remote malfunction or other fault, automatic return to aircraft take-off point over, waiting, if the failure to lift, according to ground control personnel to continue the flight, otherwise automatic parachute recovery.
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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

Retrieval SWE data are available open access and cover the whole world; however, the use of the same algorithm for different physiographic conditions results in the need to check data representativeness for each investigated territory. The aim of the study is spatial–temporal analysis of the available microwave data of passive remote sensing and estimation of the possibility of using passive microwave remote sensing for studying snow cover in European Russia. To achieve the main objective, the following tasks were completed: the comparison and objective assessment of the main methods of SWE retrieval; the estimation of the accuracy of the obtained information by the comparison of those data with the actual ground-based SWE data from meteorological stations for a long period; the assessment of the impact of variations of the basic meteorological elements on data accuracy; also the major factors influencing the uncertainty in European Russia are revealed and estimated. The estimation was carried out for the period from 2002 until 2010 with the use of remote sensing SWE data from the National Snow and Ice Data Center through their comparison with ground-based data from meteorological stations.
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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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Remote Sensing in Agriculture

Remote Sensing in Agriculture

II. DEFINITION OF REMOTE SENSING The term remote sensing was coined by Fischer in 1960 A.D. Remote sensing is defined as the art and science of gathering information about objects or areas from a distance without having physical contact with objects/areas being investigated. Remote sensing is the science and technology of making inferences about material objects from measurement made at a distance without coming into physical contact with the object under study. Remote sensing is a tool to monitor the earth’s resources using space technology in addition to ground observations. It can be used in soil mapping, land use pattern, forest mapping, geological and hydrological purpose, drought & flood monitoring in addition to crop coverage crop output estimates. Remote sensing techniques are used in agriculture & allied fields. This involves collection of basic data, monitoring of crop growth, soil moisture condition irrigation drainage & outbreak of pest & disease infestation. Spectral reflectance:
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