33,949 Hits in 6.3 sec

Distributed Computing for Remotely Sensed Data Processing [Scanning the Section]

Jon Atli Benediktsson, Zebin Wu
2021 Proceedings of the IEEE  
Recent Developments in Parallel and Distributed Computing for Anomaly Detection From Hyperspectral Remote Sensing Imagery by Q. Du, B. Tang, W. Xie, and W.  ...  Plaza Parallel and Distributed Computing for Remotely Sensed Big Data Processing by Z. Wu, J. Sun, Y. Zhang, Z. Wei, and J. Chanussot  ...  His research interests include hyperspectral image processing, parallel computing, and remotely sensed big data processing. Dr.  ... 
doi:10.1109/jproc.2021.3094335 fatcat:cupfazafdzhtpiiuuapriatota

Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics [chapter]

Mustafa Kemal Pektürk, Muhammet Ünal
2018 Data Mining  
In this chapter, we introduce a novel high-performance computing system on the geo-distributed private cloud for remote sensing applications, which takes advantages of network topology, exploits utilization  ...  The incredible increase in the volume of data emerging along with recent technological developments has made the analysis processes which use traditional approaches more difficult for many organizations  ...  In particular, the recent developments in remote sensing technologies have had a tremendous increase in remote sensor data [8] .  ... 
doi:10.5772/intechopen.75934 fatcat:fqovcfuzpngmzat2s3sai5rhte


C. Wang, F. Hu, X. Hu, S. Zhao, W. Wen, C. Yang
2015 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner.  ...  With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment.  ...  The authors are grateful to their colleagues for their constructive comments and suggestions in writing this article.  ... 
doi:10.5194/isprsannals-ii-4-w2-63-2015 fatcat:35tnrfyixffmncfxv5ov5kwr7a

Remote sensing big data computing: Challenges and opportunities

Yan Ma, Haiping Wu, Lizhe Wang, Bormin Huang, Rajiv Ranjan, Albert Zomaya, Wei Jie
2015 Future generations computer systems  
In this paper, we give a brief overview on the Big Data and data-intensive problems, including the analysis of RS Big Data, Big Data challenges, current techniques and works for processing RS Big Data.  ...  h i g h l i g h t s • This paper identifies the properties and features of remote sensing big data. • This paper reviews the stat-of-the-arts of remote sensing big data computing. • This paper discusses  ...  Introduction The recent advances in remote sensing (RS) and computer techniques give birth to the explosive growth of remote sensing (RS) data.  ... 
doi:10.1016/j.future.2014.10.029 fatcat:xw3ssuxmrfdqhi55jnepxig6by

A Survey of Big Data Analytics for Smart Forestry

Weitao Zou, Weipeng Jing, Guangsheng Chen, Yang Lu, Houbing Song
2019 IEEE Access  
In this paper, we summarize the research and work of the big data in smart forestry in recent years.  ...  Forestry big data has brought a new solution to the difficulties encountered in the course of forestry development, which refers to the application of big data technology to forestry data processing.  ...  In recent years, big data technology related to the forestry data, including the research of remote sensing big data and spatial big data, has developed rapidly, such as Hadoop, Spark, Storm [29] , and  ... 
doi:10.1109/access.2019.2907999 fatcat:nfnhvehklfbt7iiuj7orwluare


S. Saupi Teri, I. A. Musliman, A. Abdul Rahman
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
This paper summarizes parallel processing concept and architecture, the development of GPU geoprocessing for big geodata ranging from remote sensing and 3D modelling to smart cities studies.  ...  In recent years, GPU processing has improved far more GIS applications than using CPU alone.  ...  Remote Sensing The use of GPU in remote sensing has presented significant computing problems as remote sensing datasets have grown in term of capacity.  ... 
doi:10.5194/isprs-archives-xlvi-4-w3-2021-295-2022 fatcat:jag5ozvybfaprdtgoykqmyutea


S. Eken, E. Aydın, A. Sayar
2017 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface.  ...  Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered.  ...  Mamta et al. (2013) reviewed recent development in high performance computing (HPC) technology for satellite data processing.  ... 
doi:10.5194/isprs-annals-iv-4-w4-209-2017 fatcat:qllwbb2vb5b3zc5h7prcjx5qpq

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Jun Li, Zhenjie Liu, Xinya Lei, Lizhe Wang
2021 Proceedings of the IEEE  
Distributed computing strategies in remote sensing techniques and applications that use various data sources are comprehensively reviewed.  ...  A new distributed fusion framework that can accelerate the fusion of heterogeneous remote sensing and social media data is proposed by decomposing large data sets into small ones and processing them in  ...  Although there are some reviews on parallel and distributed computing for remote sensing data processing [7] , [13] , a specific review focused on the distributed fusion of remote sensing data and other  ... 
doi:10.1109/jproc.2021.3079176 fatcat:gk2xqgsipjfr7kfanauymtk724

Key Technologies Research on Building a Cluster-Based Parallel Computing System for Remote Sensing [chapter]

Guoqing Li, Dingsheng Liu
2005 Lecture Notes in Computer Science  
Moreover, it is friendly for experts who know remote sensing applications well and parallel computing less in developing their own parallel application implementations.  ...  Remote sensing image processing needs high performance computing to answer the fast growing data and requirement.  ...  However, the limit of processing speed and processing scale has been the bottleneck for remote sensing development and application.  ... 
doi:10.1007/11428862_66 fatcat:pdn3zchcvvh6redp7jreehj4me

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

Gabriele Cavallaro, Morris Riedel, Matthias Richerzhagen, Jon Atli Benediktsson, Antonio Plaza
2015 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made  ...  and parallel processing techniques.  ...  ACKNOWLEDGMENT The authors would like to thank Otto Buechner in particular and the JUDGE team at JSC in general.  ... 
doi:10.1109/jstars.2015.2458855 fatcat:o6z3ugppenblvhp7crcob2z73a

Cloud Computing in Remote Sensing: Big Data Remote Sensing Knowledge Discovery and Information Analysis

Yassine SABRI, Fadoua Bahja, Aouad Siham, Aberrahim Maizate
2021 International Journal of Advanced Computer Science and Applications  
With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data.  ...  It uses the long time-series remote sensing production process and analysis as examples to evaluate the performance of a feature data cube and distributed execution engine.  ...  INTRODUCTION In recent decades, the remarkable developments in Earth observing(EO) technology provided a significant amount of remote sensing(RS) data openly available [1] .  ... 
doi:10.14569/ijacsa.2021.01205104 fatcat:iczi4zladvfbdgaf2u4m43eyw4


Z. Ghaemi, M. Farnaghi, A. Alimohammadi
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing  ...  Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality.  ...  In a recent work, a MapReduce-based distributed SVM ensemble algorithm has been utilized for image classification. Each SVM is trained in parallel using a cluster of computers.  ... 
doi:10.5194/isprsarchives-xl-1-w5-215-2015 fatcat:cyib2ij3ifczpdgwaxrrnnnarq

Multi-Objective Task Scheduling for Energy-Efficient Cloud Implementation of Hyperspectral Image Classification

Jin Sun, Heng Li, Yi Zhang, Yang Xu, Yaoqin Zhu, Qitao Zang, Zebin Wu, Zhihui Wei
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Cloud computing has become a promising solution to efficient processing of remotely sensed big data, due to its high-performance and scalable computing capabilities.  ...  We first present a parallel computing mechanism for a fusion-based classification method based on Apache Spark.  ...  Cloud-Based Remote Sensing Big Data Processing With the development of remote sensing satellites and sensing instruments, the amount of remotely sensed data is increasing at an extremely fast velocity.  ... 
doi:10.1109/jstars.2020.3036896 fatcat:lpopweco6zejthx6buix4fevce


D. Tang, X. Zhou, Y. Jing, W. Cong, C. Li
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The data center is a new concept of data processing and application proposed in recent years.  ...  and sovled the need for concurrent multi-user high-speed access to remotely sensed data.  ...  management and processing of remote sensing data.  ... 
doi:10.5194/isprs-archives-xlii-3-1639-2018 fatcat:vepdvowkvvfjllzwrwlxseskui

Guest editorial: big spatial data

Raju Vatsavai, Varun Chandola
2016 Geoinformatica  
Big data is currently the hottest topic for data researchers and scientists with huge interests from the industry and federal agencies alike, as evident in the recent White House initiative on BBig data  ...  Big data, often characterized in terms of volume, velocity, variety, and veracity, is impacting the traditional data storage and processing frameworks.  ...  Remote sensing data represents a prime example of big spatial data.  ... 
doi:10.1007/s10707-016-0269-7 fatcat:jt53iqmzrngkfcq4n4ixikchhu
« Previous Showing results 1 — 15 out of 33,949 results