Representation-based　target　detectors　for　hyperspectral　imagery　(HSI)　have　recently　aroused　a　lot　of　interests.　However,　existing　methods　ignore　the　dictionary　structure　and　cannot　guarantee　an　informative　and　discriminative　representation　of　test　pixels　for　target　detection.　To　alleviate　the　problem,　this　letter　proposes　a　novel　sparse　and　dense　hybrid　representation-based　target　detector　(SDRD).　The　proposed　detector　adopts　the　idea　that　the　relationship　between　the　background　and　the　target　sub-dictionaries　is　a　collaborative　competition.　The　structure　of　the　dictionary　is　discovered　and　preserved　by　learning　a　sparse　and　dense　hybrid　representation　for　test　pixel.　Benefitting　from　this,　a　compact　and　discriminative　representation　can　be　obtained　to　better　represent　the　test　pixel　for　an　improved　detection　performance.　Experimental　results　on　several　HSI　data　sets　verify　the　effectiveness　of　SDRD　in　comparison　with　several　state-of-the-art　methods.