Abstract
Marine remote sensing (MRS) data provide an important tool for advancing global change research. However, the existing product service practices are insufficient for meeting the needs of a full-experience online application. This paper introduces a framework named SatANA, which is unified by a data tiling method with a spatial-aware feature, for integrated and intelligent improvements in visualization, storage and computing. The SatANA framework is supported by a hybrid database storage ideal for the cloud storage of massive MRS data. The raw data are displayed and roamed on a virtual globe through the Internet as tiles, enhancing their spatial awareness, that can be intelligently used for visualization result tuning, data storage preloading and distributed computing optimized indexing. To verify its feasibility and effectiveness, we applied this framework to a platform called SatCO2, which is devoted to providing convenient access to and the efficient utilization of MRS data.
Highlights
Earth observation satellites provide a unique source of information to address several challenging questions in the field of Earth system science [1]
To pprroommootte tthhee sshhaarriinngg aanndd mmuullttiiddiissciplinary applications of marine remote sensing (MRS) data, we developed the SatCO2 platform, which is a freely distributed piece of software adopting the SatANA framework that is devoted to meeting the needs of multisource data processing, application analyses and vivid visualization
The objective of this paper is to propose an intelligent tile-based framework for easy access to and efficient analyses of MRS data
Summary
More than 200 on-orbit satellites are currently capturing continuous Earth observations, and with a sharp increase in the number of active and passive remote sensors being sent to space, users and service providers in the remote sensing field are increasingly faced with data handling problems [4,5,6]. To meet these challenges, new approaches are required for the management, analysis and distribution of remote sensing data and products [7]. SatANA for the online analyInsisreocfoMgnRitSiodnatoaf ttohepsreoivsisdueeus,stehriesxppaepreiernpcreesstehnatts iantfergamraeteweoarskyndaamtaeadcSceastsA, NhiAghf-opretrhfoeromnalinncee acnalacluylsaistioonf ManRdS vdiavtiadtvoipsuroalvizidaetiounse. rWeexpinerteienndcetos tmhaatkienitnegtergartaeteeadsyanddatiantaeclcliegsesn, thiimghp-rpoevrfeomrmenatns cien ctahlecufolalltoiowninagndasvpievcitds:v(1is)utahleizuantiiofine.dWmeaninatgeenmdetnotmofamkeuilntitseogurracteeddaatnad, (i2n)ttehlleigoennlitniemvpirsouvaelimzaetniotsnionf tvhoelufomlleowdaintag aasnpdec(t3s): t(h1e) tehfeficuineinfitecdommapnuatignegmoefntmoafsmsivueltidsoautar.ceAdsaatap, (r2o)otfhoefocnolninceepvti,suSaaltiCzOat2ionis oimf vpolelummenetdedatatoanddem(3o) nthsteraetffie ctiheentfceoamsibpiulittiynganodf mefafsescitviveednaetsas. oAfs taheprSoaotfAoNf Aconfrcaempte, wSaotrCkO. 2Thise irmemplaeimndenertedotfo dtheims opnasptrearte itshesfterauscibtuilrietydanads efffoelclotiwvesn. esSseoctfitohne S2atAdNesAcrfibraems etwhoerkf.oTuhnedaretimonainadnedr oimf tphliesmpeanpteartiiosnstoruf cthtuerSedatAasNfoAllofrwams.eSwecotrikonin2ddeetsacilr.ibSeescttihoenf3ouinntdroadtiuonceasntdheimSaptlCemO2enptlaattifoonrmofathned SsahtoAwNsAthfreamsuepweorirokriitnydoeftatihl.eSeScattiAonN3AinftrraomduewceosrtkheaSnadtCthOe2 SpalatCtfOor2mpalnadtfosrhmowtshrthoeugsuhpseormioreitcyaosef tehxepSearitmANenAtsf.rSaemcteiwonor4kdainscdutshseesSafutCtuOre rpelsaetfaorrcmh dthirreocutgiohnsso, manedcSaseecteioxnpe5ricmonencltus.dSeesctthioenp4apdeisrc. usses future research directions, and Section 5 concludes the paper
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