This　paper　addresses　the　challenge　of　hyperspectral　anomaly　detection　(HAD)　in　noisy　environments,　where　traditional　methods　often　assume　noise-free　data　or　treat　denoising　and　detection　as　separate　processes.　Hyperspectral　images　(HSI)　are　prone　to　various　noise　types,　such　as　Gaussian　noise,　salt-and-pepper　noise,　and　others,　which　degrade　image　quality　and　hinder　accurate　anomaly　detection.　In　this　paper,　a　unified　model　is　proposed　that　integrates　denoising　and　anomaly　detection　within　the　same　network　structure,　combining　Autoencoder　and　Transformer　architectures.　For　the　entire　method　workflow,　the　network　is　first　trained　to　develop　a　denoising　model.　Subsequently,　the　same　network　is　utilized　to　train　the　HAD　model.　Specifically,　a　Transformer　is　embedded　into　the　hidden　layer　of　the　autoencoder　to　leverage　both　pixel-level　spectral　and　spatial　information　for　distinguishing　between　background　and　anomalies,　while　utilizing　its　reconstruction　capability　for　denoising.　Additionally,　a　local　multi-scale　feature　extraction　module　is　introduced　to　capture　anomaly　features　across　different　spatial　scales,　working　alongside　the　Transformer　to　explore　both　global　and　local　feature　information.　Furthermore,　the　self-attention　mechanism　is　enhanced　through　the　use　of　a　normalized　dot　product,　improving　the　capture　of　global　information　and　effectively　addressing　noise　interference　and　multi-scale　features.　Extensive　experiments　conducted　on　six　datasets　with　varying　levels　of　Gaussian　noise　and　Salt　and　Pepper　noise　indicate　that　our　method　demonstrates　excellent　detection　performance　in　noisy　environments　compared　to　other　state-of-the-art　detectors.