The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

Chen, Xi; Zhou, Lei

doi:10.1117/12.2207457

Cited by 2 publications

(2 citation statements)

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“…Some existing work points to the potential advantages that are obtainable in cloud processing of big geospatial data. For example, Chen and Zhou [22] demonstrated that Apache Hadoop can be leveraged for partitioning using a mean shift algorithm. With a local mode test, they successfully increased the processing speed by~2 times [22].…”

Section: Cloud Computing For Processing Remotely Sensed Imagesmentioning

confidence: 99%

“…For example, Chen and Zhou [22] demonstrated that Apache Hadoop can be leveraged for partitioning using a mean shift algorithm. With a local mode test, they successfully increased the processing speed by~2 times [22]. Also, Giachetta [23] introduced a Hadoop-based geospatial data management and processing toolkit, AEGIS, which he compared against many existing MapReduce-based frameworks, such as SpatialHadoop, Hadoop-GIS, HIPI, and MrGeo with spatial join, query, and aggregation operations [23].…”

Section: Cloud Computing For Processing Remotely Sensed Imagesmentioning

confidence: 99%

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Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data

2018

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Inquiry using data from remote Earth-observing platforms often confronts a straightforward but particularly thorny problem: huge amounts of data, in ever-replenishing supplies, are available to support inquiry, but scientists’ agility in converting data into actionable information often struggles to keep pace with rapidly incoming streams of data that amass in expanding archival silos. Abstraction of those data is a convenient response, and many studies informed purely by remotely sensed data are by necessity limited to a small study area with a relatively few scenes of imagery, or they rely on larger mosaics of images at low resolution. As a result, it is often challenging to thread explanations across scales from the local to the global, even though doing so is often critical to the science under pursuit. Here, a solution is proposed, by exploiting Apache Spark, to implement parallel, in-memory image processing with ability to rapidly classify large volumes of multiscale remotely sensed images and to perform necessary analysis to detect changes on the time series. It shows that processing on three different scales of Landsat 8 data (up to ~107.4 GB, five-scene, time series image sets) can be accomplished in 1018 seconds on local cloud environment. Applying the same framework with slight parameter adjustments, it processed same coverage MODIS data in 54 seconds on commercial cloud platform. Theoretically, the proposed scheme can handle all forms of remote sensing imagery commonly used in the Earth and environmental sciences, requiring only minor adjustments in parameterization of the computing jobs to adjust to the data. The authors suggest that the “Spark sensing” approach could provide the flexibility, extensibility, and accessibility necessary to keep inquiry in the Earth and environmental sciences at pace with developments in data provision.

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Section: Cloud Computing For Processing Remotely Sensed Imagesmentioning

confidence: 99%