A remote sensing image cloud processing system based on Hadoop

This article made a detailed introduction to the design and implementation of image processing algorithm framework, with its focus on data-driven, image processing algorithm, UI and processing dialog optimization. Implemented by the C++ programming language, the final framework provides a new way and reference to the Customizable development of remote sensing image processing algorithm.The remote sensing image processing system is a set of technologies and methodoloies that can add enormous value in handling the problem of the earth sciences and related fields.Most practitioners are, primarily, professionals in fields such as hydrology, agriculture, engineering, forestry, geology and urban studies; giving them the writing remote sensing processing algorithm skills considerably enhances their expertise and capablities.

A new remote sensing image processing system's algorithm module has been introduced in this paper, which is coded with Visual C++ 6.0 program language and can process large volume of remote sensing image. At the same time, adopted key technologies in algorithm module are given. Two defects of American remote sensing image processing system called ERDAS has been put forward in image filter algorithm and the storage of pixel values that are out of data type range. In author's system two optimized methods has been implemented in these two aspects. By contrasted with ERDAS IMAGINE System, the two methods had been proved to be effective in image analysis.

This paper mainly studies the low-altitude UAV (unmanned aerial vehicle) remote sensing digital processing system. The system is mainly a set of digital remote sensing image processing system developed for the data acquired by the system. First, it describes the basic principles of photogrammetry on which the system is based, and mainly explains the advantages of the beam method area network adjustment. This article briefly describes the common methods of surveying technology for power transmission and transformation lines. UAV aerial survey technology and GIS platform are applied to electric power surveys; the UAV aerial survey data are used as the data source to establish a GIS database, and the detailed system design of the structure, interface, and database of the electric power survey geographic information system is carried out. The secondary development realizes the main functions of the system such as data management, spatial query, surface analysis, and spatial analysis, facilitating scientific information management and comprehensive analysis, providing auxiliary decision-making, and bringing new methods to electric power surveys. Secondly, this article points out that the system uses an ordinary digital camera with small image size and large distortion. This article is based on the basic principles of photogrammetry, and it is worthwhile to use navigation GPS. Thirdly, the accuracy of the aerial triangulation results of the system and the accuracy of the digital orthophoto were analyzed through a large amount of data.

Aircraft detection is the challenging task in optical remote sensing image processing system. Due to the shadow affection, the accuracy of detection results getting lower. Therefore, a method of the land aircraft detection which based on shadow affection in optical remote sensing image has been proposed. First, utilizing the anisotropic diffusion method to filter the noise in image, and analyzing the characteristics of gray-scale image to locate shadow regions and candidate target areas. Then, segmentation is performed on the filter able image to highlight the target. Next, we use the morphology operation to strengthen the target area connectedness. Finally, making full use of the aircraft features for target representation, it will reflecting the aircraft target prior-knowledge, at the same time, the accuracy of detection results will be greatly enhancement. Experimental results reveal the feasibility and validity of the proposed approach in detection aircrafts in optical remote sensing images.

Summary Reverse-Time Migration (RTM) is routinely used for depth velocity model building and imaging in deepwater exploration around the world. In this study, we employed analytical redatuming to improve the efficiency of true-amplitude RTM for deepwater Earth model. This method analytically redatums both the source and receiver wavefields from surface to water bottom and injects the redatumed wavefield into the computational domain while preserving true-amplitude in the migrated common-shot image. By using analytic redatuming in deepwater area, the total runtime, memory and disk space requirements are significantly reduced. In addition, the image quality is improved by overcoming the singularity of point-source injection in the finite difference method, accurate positioning of source and receivers, and the reduction of numerical dispersion noise. The underlying theories developed in this paper can be extended to some other important applications such as Full-Waveform Inversion (FWI), true-amplitude layer-stripping RTM/FWI.

Remote-sensing image processing applications demand large-scale, collaborative processing and storage capabilities. Computational grid can provide abundant powerful computational resources with sharing and cooperation in grid environment. This paper gives an introduction to a collaborative processing mechanism based on workflow technology implemented in ImageGrid. The main idea is to encapsulate all parallel algorithms of remote-sensing image processing into grid services and recombine them with grid workflow to satisfy more and more complex remote-sensing image processing applications. In particular, we describe the architecture of the workflow-based grid system and the processing flow. We also give out the algorithm classification and an example of remote-sensing image processing grid application.

The growth of the Web has resulted in the Web‐based sharing of distributed geospatial data and computational resources. The Geospatial Processing Web (GeoPW) described here is a set of services that provide a wide array of geo‐processing utilities over the Web and make geo‐processing functionalities easily accessible to users. High‐performance remote sensing image processing is an important component of the GeoPW. The design and implementation of high‐performance image processing are, at present, an actively pursued research topic. Researchers have proposed various parallel strategies for single image processing algorithm, based on a computer science approach to parallel processing. This article proposes a multi‐granularity parallel model for various remote sensing image processing algorithms. This model has four hierarchical interfaces that are labeled the Region of Interest oriented (ROI‐oriented), Decompose/Merge, Hierarchical Task Chain and Dynamic Task interfaces or sub‐models. In addition, interfaces, definitions, parallel task scheduling and fault‐tolerance mechanisms are described in detail. Based on the model and methods, we propose an open‐source online platform named OpenRS‐Cloud. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi‐granularity parallel model for unified remote sensing image processing web services.

Cloud computing has emerged as a transformative technology, offering diverse service models to meet varying organizational needs. This study employs the Complex Proportional Assessment (COPRAS) method to comprehensively evaluate and analyze different cloud environments, providing a nuanced approach to cloud service selection. The research investigates five distinct cloud environment types: general-purpose, service-centric, zone-centric, distance-centric, and cost-centric, examining their performance across critical parameters including quality of service, number of available services, availability zones, consumer distance, and hourly cost. The multi-criteria analysis reveals significant variations in cloud environment effectiveness. Zone-centric environments emerged as the top performer, achieving a remarkable quality index of 0.291 and a 100% usability degree. Service-centric environments followed closely, demonstrating a 92.03% usability degree and highlighting the importance of service availability. General-purpose environments showed moderate performance with a 63.97% usability degree, indicating their versatility. Conversely, distance-centric and cost-centric environments exhibited the lowest performance, suggesting limitations in meeting comprehensive organizational requirements. Key findings underscore that cloud computing is not a one-size-fits-all solution but a complex ecosystem requiring strategic selection. The research emphasizes that while cost is important, it should not be the sole determining factor in cloud environment selection. The study provides a robust framework for decision-makers, enabling them to align cloud infrastructure choices with specific organizational objectives. The methodology offers critical insights into the evolving cloud computing landscape, addressing the growing complexity of data management and the increasing demand for scalable, secure computing solutions. By presenting a comprehensive evaluation approach, the research contributes to a more sophisticated understanding of cloud environment selection, encouraging organizations to adopt a strategic, multi-dimensional approach to cloud infrastructure deployment.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

A cost effective, easy to manage, elastic and powerful resources over the internet is provided to the users by cloud environment. The resource pooling allows the use of same pool by multiple users through multi tenancy and virtualization technology. The VM Migration is the process of relocating a VM to another physical machine without shutting down the VM. The Virtual Migration is carried out for the number of reasons such as load balancing, fault tolerance and maintenance with respect to pooling and memory space management. Memory space prediction method used to predict the free memory space and allocate the memory to the new processes or the requested user. Memory allocation then becomes a selection function of selecting appropriate memory space according to the user needs a capacity. The free memory spaces are listed in the free list. It's helpful for easy allocation. During the migration phase, the contents of the VM are exposed to the network that might lead to data privacy and integrity concerns. Besides data, the space used by VM for storage and communication is also vulnerable to attackers during migration. To reduce the data loss due to attack on memory space to increase the confidentiality of the data which is migrated, here it is designed and implemented a new scheme for Virtual Machine Migration using Memory Space Prediction with compression method for storage using a cryptic methodology. The prediction based compression algorithm used to transfer data in compressed manner, it also decreases the migration time and down time and the hash algorithm used to increases the data confidentiality and security.

Now-a-days, cloud computing has become a cornerstone of modern technology, driving innovation, efficiency and accessibility across various industries and application. Distributed computing solves the difficulty of using distributed autonomous machines and communicating with each other over a network. Cloud computing provides clients with a range of services and capabilities that enhances productivity, accessibility and scalability while reducing the need for extensive hardware and infrastructure investments. When interest in distributed computing rises, it implies that more individuals, businesses, and organizations are exploring, adopting, and implementing distributed computing solutions. This surge in interest leads to increase in data traffic. There are two solutions for the issue of increase in data traffic, one is to server optimization (or) server performance enhancement (upgrade single server to a high performance server) but upgraded server may exceed its capacity (overload).Another one is multi servers. Ulti-server configurations are common in scenarios where the demands of an application or service exceed the capabilities of a single server, and the distribution of tasks across multiple servers is necessary for optimal performance and reliability. When there are multiple servers, there is an issue i.e. Load Adjusting. [1] Load balancing is one of the critical issues in cloud computing. In a cloud environment, where resources are often dynamically allocated and distributed, load balancing plays a central role in managing workloads efficiently. Load balancing in cloud computing is a technique used to distribute computing workloads and network traffic across multiple servers or resources within a cloud environment. The primary goal of load balancing is to optimize resource utilization, prevent individual servers from becoming overloaded, and ensure that the overall system can handle varying levels of demand efficiently. Here we also discussed about the hybrid of Cat and Mouse Optimization and Grey Wolf Optimization algorithms. This paper refers to Cloud Computing, Load balancing techniques, Load balancing algorithms.

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops.

Cloud computing not only provides convenience for users, but also brings certain information security threats to users. How to effectively protect the user’s information security has become one of the core issues of network security. This paper analyzes the problems and reasons of network security in cloud computing environment, and points out the main ways to improve network security. This paper analyzes the network security problems in the cloud computing environment, and discusses the information security technology in the network. This paper studies the definition, characteristics, classification and core technology of cloud computing security, analyzes the security risk of cloud computing network, and puts forward the cloud computing network security model and security level evaluation method from different angles. Level experiment results show that the network security model in cloud environment is feasible, computing can fully meet the development requirements of the current network information age, and effectively standardize the network security operation environment. The results show that: in 2016, the number of articles related to cloud computing is at least 2653, and in 2020, the number of articles related to cloud computing is at most 6438.

Cloud Computing and Forensic Accounting: Friends or Foes?

Due to economic benefits of cloud computing, consumers have rushed to adopt Cloud Computing. Apart from rushing into cloud, security concerns are also raised. These security concerns cause trust issue in adopting cloud computing. Enterprises adopting cloud, will have no more control over data, application and other computing resources that are outsourced from cloud computing provider. In this paper we propose a novel technique that will not leave consumer alone in cloud environment. Firstly we present theoretical analysis of selected state of the art technique and identified issues in IaaS cloud computing. Secondly we propose Distributed Trust Protocol for IaaS Cloud Computing in order to mitigate trust issue between cloud consumer and provider. Our protocol is distributed in nature that lets the consumer to check the integrity of cloud computing platform that is in the premises of provider's environment. We follow the rule of security duty separation between the premises of consumer and provider and let the consumer be the actual owner of the platform. In our protocol, user VM hosted at IaaS Cloud Computing uses Trusted Boot process by following specification of Trusted Computing Group (TCG) and by utilizing Trusted Platform Module (TPM) Chip of the consumer. The protocol is for the Infrastructure as a Service IaaS i.e. lowest service delivery model of cloud computing.