In　remote　sensing　of　Earth　observation,　multi-source　data　can　be　captured　by　multiple　platforms,　multiple　sensors,　and　multiple　perspectives.　These　data　provide　complementary　information　for　interpreting　remote　sensing　scenes.　Although　these　data　offer　richer　information,　they　also　increase　the　demand　for　model　depth　and　complexity.　Deep　learning　plays　a　pivotal　role　in　unlocking　the　potential　of　remote　sensing　data　by　delving　deep　into　the　semantic　layers　of　scenes　and　extracting　intricate　features　from　images.　Recent　advancements　in　artificial　intelligence　have　greatly　enhanced　this　process.　However,　deep　learning　networks　have　limitations　when　applied　to　remote　sensing　images.　1)The　huge　number　of　parameters　and　the　difficulty　in　training,　as　well　as　the　over-reliance　on　labeled　training　data,　can　affect　these　images.　Remote　sensing　images　are　characterized　by“data　miscellaneous　marking　difficulty”,　which　makes　manual　labeling　insufficient　for　meeting　the　training　needs　of　deep　learning.　2)Variations　in　remote　sensing　platforms,　sensors,　shooting　angles,　resolution,　time,　location,　and　weather　can　all　impact　remote　sensing　images.　Thus,　the　interpreted　images　and　training　samples　cannot　have　the　same　distribution.　This　inconsistency　results　in　weak　generalization　ability　in　existing　models,　especially　when　dealing　with　data　from　different　distributions.　To　address　this　issue,　cross-domain　remote　sensing　scene　interpretation　aims　to　train　a　model　on　labeled　remote　sensing　scene　data(source　domain)and　apply　it　to　new,　unlabeled　scene　data(target　domain)in　an　appropriate　way.　This　approach　reduces　the　dependence　on　target　domain　data　and　relaxes　the　assumption　of　the　same　distribution　in　existing　deep　learning　tasks.　The　shallow　layers　of　convolutional　neural　networks　can　be　used　as　general-purpose　feature　extractors,　but　deeper　layers　are　more　task-specific　and　may　introduce　bias　when　applied　to　other　tasks.　Therefore,　the　migration　model　must　be　modified　to　accomplish　the　task　of　interpreting　the　target　domain.　Cross-domain　interpretation　tasks　aim　to　establish　a　model　that　can　adapt　to　various　scene　changes　by　utilizing　migration　learning,　domain　adaptation　and　other　techniques　for　reducing　model　prediction　inaccuracy　caused　by　changes　in　the　data　domain.　This　approach　improves　the　robustness　and　generalization　ability　of　the　model.　Interpreting　cross-domain　remote　sensing　scenes　typically　requires　using　data　from　multiple　remote　sensing　sources,　including　radar,　aerial　and　satellite　imagery.　These　images　may　have　varying　views,　resolutions,　wavelength　bands,　lighting　conditions　and　noise　levels.　They　may　also　originate　from　different　locations　or　sensors.　As　the　Global　Earth　Observation　Systems　continues　to　advance,　remote　sensing　images　now　include　cross-platform,　cross-sensor,　cross-resolution,　and　cross-region,　which　results　in　enormous　distributional　variances.　Therefore,　the　study　of　cross-domain　remote　sensing　scene　interpretation　is　essential　for　the　commercial　use　of　remote　sensing　data　and　has　theoretical　and　practical　importance.　This　report　categorizes　scene　decoding　tasks　into　four　main　types　based　on　the　labeled　set　of　data：methods　based　on　closed-set　domain　adaptation,　partial-domain　adaptation,　open-set　domain　adaptation　and　generalized　domain　adaptation.　Approaches　based　on　closed-set　domain　adaptation　focus　on　tasks　where　the　label　set　of　the　target　domain　is　the　same　as　that　of　the　source　domain.　Partial　domain　adaptation　focuses　on　tasks　where　the　label　set　of　the　target　domain　is　a　subset　of　the　source　domain.　Open-set　domain　adaptation　aims　to　research　tasks　where　the　label　set　of　the　source　domain　is　a　subset　of　the　label　set　of　the　target　domain,　and　it　does　not　apply　restrictions　in　the　approach　of　generalized　domain　adaptation.　This　study　provides　an　in-depth　investigation　of　two　typical　tasks　in　cross-domain　remote　sensing　interpretation：scene　recognition　and　target　knowledge.　The　first　part　of　the　study　utilizes　domestic　and　international　literature　to　provide　a　comprehensive　assessment　of　the　current　research　status　of　the　four　types　of　methods.　Within　the　target　recognition　task,　cross-domain　tasks　are　further　subdivided　into　cross-domain　for　visible　light　data　and　cross-domain　from　visible　light　to　Synthetic　Aperture　Radar　images.　After　a　quantitative　analysis　of　the　sample　distribution　characteristics　of　different　datasets,　a　unified　experimental　setup　for　cross-domain　tasks　is　proposed.　In　the　scene　classification　task,　the　dataset　is　explored　by　classifying　it　according　to　the　label　set　categorization,　and　specific　examples　are　given　to　provide　the　corresponding　experimental　setup　for　the　readers’reference.　The　fourth　part　of　the　study　discusses　the　research　trends　in　cross-domain　remote　sensing　interpretation,　which　highlights　four　challenging　research　directions：few-shot　learning,　source　domain　data　selection,　multi-source　domain　interpretation,　and　cross-modal　interpretation.　These　areas　will　be　important　directions　for　the　future　development　of　remote　sensing　scene　interpretation,　which　offers　potential　choices　for　readers’subsequent　research　directions.　©　2024　Editorial　and　Publishing　Board　of　JIG.　All　rights　reserved.