Unsupervised　domain　adaptation　(UDA)　is　critical　for　remote　sensing　object　detection　in　real　applications,　aiming　to　address　the　significant　performance　degradation　issue　caused　by　the　domain　gap　between　the　source　and　target　domain.　This　method　achieves　cross-domain　alignment　by　leveraging　the　unlabeled　target　domain　data,　thus　avoiding　the　expensive　annotation　cost.　However,　existing　works　mainly　cope　with　convolutional　neural　network　(CNN)-based　object　detectors,　which　are　characterized　by　complex　adversarial　learning　architecture　and　fail　to　accurately　align　the　features　in　remote　sensing　images　with　sparsely　allocated　objects　and　inevitable　background　noise.　Compared　to　CNN-based　methods,　the　detection　transformer　(DETR)　largely　simplifies　the　object　detection　pipeline　and　demonstrates　the　great　potential　of　its　intrinsic　characteristics　of　global　relation　modeling　between　any　pixels.　On　this　basis,　we　propose　the　first　strong　DETR-based　baseline,　remote　sensing　teacher,　for　UDA　in　remote　sensing　object　detection.　Specifically,　the　remote　sensing　teacher　introduces　an　innovative　learnable　frequency-enhanced　feature　alignment　(LFA)　module.　Within　this　module,　we　initially　transform　the　features　into　frequency　space　to　simplify　the　attention　solver　and　effectively　capture　domain-specific　information.　Subsequently,　the　module　significantly　enhances　the　global　feature　representations　of　sparsely　allocated　objects　by　using　a　lightweight　attention　mechanism.　Following　this,　the　module　incorporates　learnable　filters　with　a　gated　mechanism,　enabling　selective　alignment　of　features　in　noisy　backgrounds.　In　addition,　the　remote　sensing　teacher　employs　a　self-adaptive　pseudo-label　assigner　(SPA)　that　can　automatically　adjust　the　class-wise　confidence　threshold　according　to　the　model＇s　learning　status,　thereby　enabling　the　generation　of　high-quality　pseudo-labels　in　scenarios　with　a　long-tailed　distribution.　Leveraging　these　pseudo-labels　further　mitigates　the　domain　bias　of　the　detector　by　establishing　alignment　at　the　label　level.　Extensive　experimental　results　demonstrate　the　superior　performance　and　generalization　capabilities　of　our　proposed　remote　sensing　teacher　in　multiple　remote　sensing　adaptation　scenarios.　The　Code　is　released　at　https://github.com/h751410234/RemoteSensingTeacher.