High Resolution Satellite Image Processing Using Hadoop Framework

Rajak, Roshan; Raveendran, Deepu; Bh, Maruthi Chandrasekhar; Medasani, Shanti

doi:10.1109/ccem.2015.16

Cited by 16 publications

(11 citation statements)

References 4 publications

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“…This architecture mainly contains Namenode and Datanode. Master is identified as Namenode contains only metadata of the file system and Slave is identified as Datanode contains Application data is stored in the form of 64MB blocks [5, 18,19] on multiple Datanodes. Map-Reduce is a parallel programming runs on the upper level of the HDFS consists of one Job Tracker to which client applications submit Map Reduce jobs.…”

Section: Mapreducementioning

confidence: 99%

“…This HBase is a distributed database framework methodology which is generally used where there is in need of large tables with non-sequential, real-time read and write access. In [9] the image after processing is saved in HBase, upon which it can update the required data at any moment to resolve the multi-temporal problem saved in remote sensing image data.…”

Section: Introductionmentioning

confidence: 99%

“…This framework is formulated for parallel processing of data which are evenly distributed to the computing nodes, in a way that a Hadoop cluster is easily scalable horizontally. Hadoop framework constituted the following modules[9] as shown inTable-1.…”

mentioning

confidence: 99%

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Distributed Framework for Processing High Resolution Remote Sensing Images

Nagaraju*,

Suneetha

2019

IJEAT

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Now-a-days, sensing of remote satellite data processing is a very challenging task. The current development of satellite technology has led to explosive growth in quantity as well as the quality of the High-Resolution Remote Sensing (HRRS) images. These images can sometimes be in Gigabytes and Terabytes, which is heavy to load into the memory and also takes more time for processing. To address the challenges of processing HRRS images, a distributed map Reduce framework is proposed in this paper. This paper reflects Map-reduce as a distributed model using the Hadoop framework for processing large amounts of images. To process large amounts of images, block-based and size-based methods are introduced for effective processing. From the experiments, the proposed framework has proven to be effective in performance and speed.

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Section: Mapreducementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed Framework for Processing High Resolution Remote Sensing Images

Nagaraju*,

Suneetha

2019

IJEAT

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“…This makes the Hadoop distributed computing model more suitable to unstable wide area network environments. Rajak et al [ 11 ] store the outputting results in the HBase of the Hadoop system and perform parallel RS data processing. The speedup and performance show that by utilizing Hadoop, they can distribute the workload across different clusters.…”

Section: Introductionmentioning

confidence: 99%

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

Yang

Song

Mei

2017

Sensors

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The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

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“…In the work they do, existing libraries are made available for distributed use. (Rajak et al, 2015) presented a Hadoop map/reduce based architecture to store the program output in HBase for remote sensing satellite data. Algorithms proposed for map/reduce solution are image registration, watershed image segmentation, image mosaicing and gauss filter.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Stitching Satellite Images in a Bigdata Mapreduce Framework

Sari¹,

Eken²,

Sayar³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this study we present a two-step map/reduce framework to stitch satellite mosaic images. The proposed system enable recognition and extraction of objects whose parts falling in separate satellite mosaic images. However this is a time and resource consuming process. The major aim of the study is improving the performance of the image stitching processes by utilizing big data framework. To realize this, we first convert the images into bitmaps (first mapper) and then String formats in the forms of 255s and 0s (second mapper), and finally, find the best possible matching position of the images by a reduce function.

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High Resolution Satellite Image Processing Using Hadoop Framework

Cited by 16 publications

References 4 publications

Distributed Framework for Processing High Resolution Remote Sensing Images

Distributed Framework for Processing High Resolution Remote Sensing Images

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

An Approach for Stitching Satellite Images in a Bigdata Mapreduce Framework

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