A Relative Radiation Normalization Method of ISS Nighttime Light Images Based on Pseudo Invariant Features

Lu Bi,Weili Jiao,Boris A Portnov,Shengrong Wei,Ming Liu,Huichan Liu,Wei Jiang,Tengfei Long

doi:10.3390/rs12203349

Abstract

The International Space Station (ISS) offers a unique view from space that provides nighttime light (NTL) images of many parts of the globe. Compared with other NTL remote sensing data, ISS NTL multispectral images taken by astronauts with commercial digital single-lens reflex (DSLR) cameras have the characteristics of free access, high spatial resolution, abundant data and no light saturation, so it plays a unique advantage in the research of small-scale urban planning, optimization of lighting resource allocation and blue light pollution. In order to improve the radiation consistency of ISS NTL images, a relative radiation normalization method of ISS NTL images is proposed in this paper. Pseudo invariant features (PIF) were identified in the cloud-free Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) time series NTL remote sensing annual composite product, and then they were used to derive the relative radiation normalization model of ISS NTL images. The results show that the radiation brightness of ISS NTL images in different regions is normalized to the same gray level with that of DMSP/OLS NTL remote sensing images in the same year, which improves the radiation brightness comparability between different regions of ISS NTL images. This method is universally applicable to all ISS NTL images, which is beneficial to the NTL comparability of ISS NTL image in the regional horizontal and temporal vertical.

Highlights

Many researchers have studied the consistent calibration of Nighttime light (NTL) remote sensing data by using the pseudo-invariant feature method, and the results show that they all have good linear relationships or quadratic polynomial relationships [56]
The relative radiation normalization model is established by identifying the pseudo-invariant feature points
The results show that there is a good linear and quadratic polynomial relationship between the International Space Station (ISS) NTL image and DMSP/OLS NTL image

Summary

Introduction

Nighttime light (NTL) remote sensing is using airborne (or orbital) sensors to detect upward-directed light from the night side of the earth, whether natural or artificial in nature [1,2], such as aurorae, volcanic eruptions, marine bioluminescence, city lights, fishing boats, oil and gas wellNighttime light (NTL) remote sensing is using airborne (or orbital) sensors to detect upwardRdemiroetecSteends. l2i0g2h0t, 1f2r,o3m349the night side of the earth, whether natural or artificial in nature [1,2], suc2hoaf s21 aurorae, volcanic eruptions, marine bioluminescence, city lights, fishing boats, oil and gas well cocommbbuustsitoionn, ,eetctc. .IInnrreecceennttyyeeaarrss,,NNTTLL rreemmoottee sseennssiinngg iimmaaggeerryy hhaass ggrraadduuaallllyyccoommeeiinnttooppeeoopplele's’svvieieww bbeecacauuseseooffitistsuunniqiquueecchhaarrmmaannddiittsshhiigghh ccoorrrreellaattiioonn wwiitthh hhuummaann aaccttiivviittiieess..CCoommppaarreeddwwitihthoordrdininaaryry reremmootetesseennssininggssaatteelllliitteeiimmaaggeess,, NNTTLL rreemmoottee sseennssiinngg iimmaaggeess rreefflleecctt mmoorree hhuummaannaaccttivivitiiteiessfrfroommaa ddiffifefreernetnpt epresrpsepcetcivtiev,es,ostohethyehyahvaevbeeebneewnidweildyeulyseudsiendsioncisaol cainadl aencdoneocmonicopmaircapmaertaemr eestetirmeasttiiomna[t3io–n6], u[r3b–a6n],izuartbioannimzaotinointomrinogniatonrdinegvaanludateivoanlu[7a,t8io],nm[7a,j8o]r, dmisaajostredrsis(awstheersth(ewr hneatthuerralnoatruhraulmoarnh-cuamuasend- ) ecvaauluseadti)onev[a9l,u1a0t]i,olnigh[9t,1p0o]l,lulitgihotnpreoslleuatricohn [r1e1s–e1a5rc],hfi[s1h1e–r1y5]i,nffiosrhmeraytioinnfoerxmtraatciotinonex[1tr6a–c1ti8o]n, e[p1i6d–e1m8]i,c dtMorDemiespeeesohtifoeadnieTgosleinTuehtrhmoshtoerierelioesdiconMsnpegedgavdiaeettce[itroivas2elcaoeel3ohlhlap]rSo.is[romgap1eelth5omsee,ogrn1leesllit9uincspto,aett2eafial0tooNriPS]anc,farTlheotNLaexgrln[etrTlr1rsidaeatL5ommec,l1ftuPro/ri9Oetootr,i2menoomp0gnsooe]er,trfpaneaasmesanttixinsrdn/oteeOcrgnnehafpatshrcrielolotaniirmsmLgogahenitanixthpotsepioanasc[efsnac2ralct1iseoehn,Ltx2nrrip2eomcSn]eeemye,utdrssaiacltenaltteaniiindgmnsccpheeoStvted(iosyDclostalaMmrt[une2natm1dulSio,elP2igtnv(ni2/aDsOo]ffps,rolMLouefiramScmtSenit)oPlrdadlano/otOlfimwoofLitirnnohlsSo.nfpe)moaiaortnUtomTfidlarnhotailhinwettgreiegeoaDdsUsn[so2pe.nSl3ffuatiiTe]ttaet.niehiltaoddseelnes irseSsspttohaatleteuiasfitliriossrntetshp[oe1lla–uft4iftroi,s6ort,nm7p][.lt1ao–tAf4po,s6rrmo,t7vh]i.tedoAsepuslrcootcnhvegeisdstseoiumrlcoceone-fssgesDotriiMrmesoSefgP-slD/eoOrMbiLeaSSslP,Ng/VlOToiLbsLiaSbrl,eleNmVTIiosnLitfberrlaseeremeInndosftirneIamgrseeaidnmgsiIanimngggeasRgimiwandagitigoheRmslaoedwwtieiotrsmhpSelaouttewiitarel DSauyi/tNe iDghatyB/Nanigdh(tVBIIaRnSd/D(VNIBIR) Sp/rDovNiBd)esplroonvgidtiems el-osnegrietismgelo-sbearlieNsTgLlorebmaloNteTsLenrseimngoitme asegnessiwngithimhaiggheesr spwaittihalhaingdhetremsppaotriaall raensdoltuetmiopno[r8a]l. rAesstorlountaiounts[o8f].thAestIrnotneranuattsioonfatlhSepIancteerSntaattiioonna(IlSSSp)ahcaevSettaatkioenn m(ISaSn)y eahratvheimtaakgeens wmiatnhycoemarmthericmiaalglyesavwaiiltahblceocmammeerrcaisaslliyncaev2a0i0la0b, liencclaumdienrgasNsTinLciem2a0g0e0s, oifnscolumdeinagreNasT[L2]. Nighttime light (NTL) remote sensing is using airborne (or orbital) sensors to detect upward-directed light from the night side of the earth, whether natural or artificial in nature [1,2], such as aurorae, volcanic eruptions, marine bioluminescence, city lights, fishing boats, oil and gas well. Nighttime light (NTL) remote sensing is using airborne (or orbital) sensors to detect upwardRdemiroetecSteends. L2i0g2h0t, 1f2r,o3m349the night side of the earth, whether natural or artificial in nature [1,2], suc2hoaf s21 aurorae, volcanic eruptions, marine bioluminescence, city lights, fishing boats, oil and gas well cocommbbuustsitoionn, ,eetctc. Tmhieds-ehIigSSh NspTaLtiaiml raegseosluhtaiovne mofid5–-h2i0g0hmsppaetiralprixeseol launtidonthoref e5–b2a0n0dms opfecroploixreilnafonrdmthatrieoen (rbeadn,dgsreoefnc,oalonrdibnlfuorem) [a2t5io–2n7(]r.ed, green, and blue) [25,26,27]. TaTsheet.yThhaevye uhnaiqvue euanpiqpuleicaaptipolnicvaatiloune vinaleupeidinemepiiodleomgyiorleosgeyarrcehse[a2r8c,h29[]2,8e,2c9o]l,oegciocalolgeincavlireonnvmireonntmeeffnetcetf[f3e0c]t,[l3ig0]h,t ploigllhutipoonllauntaiolynsiasn[a2l6y–s3is0[]2,6e–c3o0n]o, meciocnaonmaliycsaisna[3ly1s]iasn[3d1]uarbnadnudreblainedaetiloinne[a3t2io–n3[43]2.

Methods

Results

Discussion

Conclusion