Remote　sensing　scene　zero-shot　object　detection　(ZSD)　aims　to　detect　and　recognize　both　seen　and　unseen　categories　of　landscape　elements　with　the　guidance　of　the　word　embeddings.　In　this　task,　two　primary　challenges　are　identified.　First,　there　exists　considerable　variability　within　categories　of　landscape　elements,　causing　a　misalignment　between　visual　features　and　word　embeddings,　particularly　noticeable　for　unseen　categories.　Second,　the　existing　detection　models　struggle　to　provide　accurate　localization　predictions,　greatly　impacting　overall　performance.　To　address　these　two　issues,　we　propose　word　embedding　alignment-DINO　(WEA-DINO).　Based　on　the　original　DINO　structure,　our　WEA-DINO-Head　is　specifically　designed　to　align　the　hidden　features　of　＂matching　queries＂　with　word　embedding　features,　effectively　addressing　the　misalignment　issue　between　visual　features　and　word　embeddings.　Furthermore,　aligning　the　hidden　features　of　＂denoising　queries＂　with　word　embedding　features　enables　the　translation　of　localization　capabilities　from　known　categories　to　previously　unseen　ones.　Through　extensive　experimentation　on　the　DIOR　benchmark　dataset,　our　method　demonstrates　state-of-the-art　(SOTA)　performance.　The　code　is　available　at　https://github.com/cv516Buaa/WEA-DINO.