Existing　sparsity-based　hyperspectral　image　(HSI)　target　detection　methods　have　two　key　problems.　1)　The　background　dictionary　is　locally　constructed　by　the　pixels　between　the　inner　and　outer　windows,　surrounding　and　enclosing　the　central　test　pixel.　The　dual-window　strategy　is　intricate　and　might　result　in　impure　background　dictionary　deteriorating　the　detection　performance.　2)　For　an　unbalanced　binary　classification　problem,　the　target　dictionary　atoms　are　generally　inadequate　compared　with　the　background　dictionary,　which　might　yield　unstable　performance.　For　the　issues,　this　article　proposes　a　novel　structurally　incoherent　background　and　target　dictionaries　(SIBTD)　learning　model　for　HSI　target　detection.　Specifically,　with　the　concept　that　the　observed　HSI　data　is　composed　of　low-rank　background,　sparsely　distributed　targets,　and　dense　Gaussian　noise,　the　background　and　target　dictionaries　can　be　jointly　derived　from　the　observed　HSI　data.　Additionally,　the　introduction　of　structural　incoherence　can　enhances　the　discrimination　between　the　target　and　background　dictionaries.　Thus,　the　developed　model　can　not　only　lead　to　a　pure　and　unified　background　dictionary　but　also　augment　the　target　dictionary　for　improved　detection　performance.　Besides,　an　efficient　optimization　algorithm　is　devised　to　solve　SIBTD　model,　and　the　performance　of　SIBTD　is　verified　on　three　benchmark　HSI　datasets　in　comparison　with　several　state-of-the-art　detectors.