Survey of current hyperspectral Earth observation applications from space and synergies with Sentinel-2

Transon, Julie; d’Andrimont, Raphaël; Maugnard, Alexandre; Defourny, Pierre

doi:10.1109/multi-temp.2017.8035244

Cited by 4 publications

(2 citation statements)

References 82 publications

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“…Also, HSI with narrow band systems are agile to produce spectral spectrum at hundreds of distinct wavelengths [ 3 ]. This spectral spectrum makes HSI a powerful and interesting tool for earth surface categorization resulting in a promising wide range of applications [ [4] , [5] , [6] ]. The HSI data consists of various channels compared to RGB or grayscale images which comprise three or more channels [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging and its applications: A review

Bhargava,

Sachdeva,

Sharma

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging and its applications: A review

Bhargava,

Sachdeva,

Sharma

et al. 2024

Heliyon

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“…Space-borne sensors conducting the continous monitoring of the earth's surface have offered a unique and accurate approach to a better understanding of our planet in the past few decades [1,2]. With the booming of high-resolution Earth Observation (EO) [3][4][5] as well as the increasing diversity of advanced sensors, remotely sensed earth observation data have undergone exponential growth [6]. The Landsat missions [7] of the U.S. Geological Survey (USGS) have archived more than 4.5 petabytes of data by 2019.…”

Section: Introductionmentioning

confidence: 99%

In-Memory Distributed Mosaicking for Large-Scale Remote Sensing Applications with Geo-Gridded Data Staging on Alluxio

Song

Zhang

2022

Remote Sensing

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The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much larger scale than ever. Imagery mosaicking, which is critical for forming “One Map” with a continuous view for large-scale climate research, has drawn significant concern. However, despite employing distributed data processing engines such as Spark, large-scale data mosaicking still significantly suffers from a staggering number of remote sensing images which could inevitably lead to discouraging performance. The main ill-posed problem of traditional parallel mosaicking algorithms is inherent in the huge computation demand and incredible heavy data I/O burden resulting from intensively shifting tremendous RS data back and forth between limited local memory and bulk external storage throughout the multiple processing stages. To address these issues, we propose an in-memory Spark-enabled distributed data mosaicking at a large scale with geo-gridded data staging accelerated by Alluxio. It organizes enormous “messy” remote sensing datasets into geo-encoded gird groups and indexes them with multi-dimensional space-filling curves geo-encoding assisted by GeoTrellis. All the buckets of geo-grided remote sensing data groups could be loaded directly from Alluxio with data prefetching and expressed as RDDs implemented concurrently as grid tasks of mosaicking on top of the Spark-enabled cluster. It is worth noticing that an in-memory data orchestration is offered to facilitate in-memory big data staging among multiple mosaicking processing stages to eliminate the tremendous data transferring at a great extent while maintaining a better data locality. As a result, benefiting from parallel processing with distributed data prefetching and in-memory data staging, this is a much more effective approach to facilitate large-scale data mosaicking in the context of big data. Experimental results have demonstrated our approach is much more efficient and scalable than the traditional ways of parallel implementing.

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